GoCreative Agent API

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the model (DeepSeek V3.1), cost range ($0.005–$0.05 USDC), payment method (USDC on Base), and that no API key is needed. It lacks details on rate limits, latency, or error handling, but is still informative.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief yet comprehensive: a one-line summary, list of use cases, example call, and cost info. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers all key aspects: purpose, usage, cost, and example. It is complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides only the parameter name and type (string). The description adds substantial meaning: it explains that the 'prompt' parameter accepts any prompt, gives an example JSON, and describes typical use cases. Fully compensates for 0% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is an LLM completion tool using DeepSeek V3.1, lists concrete use cases (summarization, extraction, classification, drafting, reasoning), and distinguishes from siblings by specifying 'standard tier' and comparing cost to 'premium model tokens'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear guidelines on when to use (e.g., for summarization, extraction) and mentions cost savings versus premium models. However, it does not explicitly contrast with ai_pro or ai_ultra, leaving some ambiguity about when to choose those alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the cost model (USDC per call, price range), the fact that no API key or signup is required, and provides an example call. It does not explain rate limits or response format, but for a simple 1-parameter tool, this is adequate transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, using 5 sentences plus a compact example. It front-loads the purpose, then adds cost and usage guidance with no wasted words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, no output schema), the description covers the essential aspects: what it does, when to use, cost, and example. It lacks explicit mention of output format (likely text) but is otherwise complete for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must add meaning to the sole parameter 'prompt.' It only provides a generic example and implies the prompt should contain instructions. It does not specify length limits, content restrictions, or format expectations, thus adding minimal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool is for LLM completion using the frontier model Claude 4.5 Sonnet, listing capabilities (reasoning, analysis, synthesis, drafting). It distinguishes from sibling tools like ai_ask and ai_ultra by emphasizing it's the 'smartest model' for hard tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use the tool 'when the task is hard enough to need the smartest model,' providing clear context. While it does not name alternative tools or state when not to use it, the guidance is strong enough for an agent to differentiate from cheaper/simpler siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses cost range ($0.005–$0.05), payment method, and model identity, but does not mention latency, rate limits, or error handling. It provides moderate behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is succinct and well-structured: a clear label, purpose, recommended use case, cost info, and an example. Every sentence adds essential information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter LLM tool with no output schema, the description covers purpose, usage conditions, cost, and example. It does not specify the return format, but that is reasonably inferred for an LLM completion tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% but description adds value through an example prompt and context (deep multi-step analysis). This helps the agent understand what kind of content to provide beyond the schema's simple 'string' type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs LLM completion using Claude Opus 4.6 for the hardest agent tasks. It uses specific verbs ('LLM completion') and distinguishes itself from siblings (ai_ask, ai_pro) by highlighting it as the top-end reasoning model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises 'Use when model quality dominates cost,' providing clear selection criteria. It also mentions the payment method (USDC per call) and gives example usage, but does not explicitly state when not to use or list alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description adds value by disclosing cost per call and providing an example input. However, it does not mention read-only nature, rate limits, data freshness, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, two sentences plus an example and cost. Every sentence adds unique value, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although the output format is not detailed, the description lists the security signals checked, which gives sufficient context for an AI agent to understand the tool's output. The simplicity (1 param) reduces the need for more detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has no description for the single parameter (0% coverage), but the description explains the format ('owner/repo') and gives an example, fully compensating for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Audit a GitHub repo for security signals' with specific signals listed, and distinguishes from sibling tools like lookup_github by focusing on security auditing for supply-chain risk.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies the use case ('OSS supply-chain risk scoring') and provides an example call, but does not explicitly mention when not to use or alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility. It discloses the cost range ($0.005–$0.05 USDC on Base) and gives an example call, but does not discuss data freshness, rate limits, or potential limitations. The behavioral disclosure is adequate but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences plus an example and cost line. It front-loads the core purpose, then provides use cases, an example, and pricing—all without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter, no output schema, and no annotations, the description provides a clear purpose, usage context, example, and cost. It is sufficient for an agent to invoke the tool correctly, though mentioning the output structure would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'domain' has no schema description (0% coverage). The description compensates by stating 'for any domain' and showing an example ('stripe.com'), implying the input format. This adds significant meaning beyond the minimal schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: combining domain intelligence (DNS/WHOIS/TLS) with company firmographics for any domain. It also lists specific use cases (sales prospecting, KYB, enrichment) and distinguishes itself from sibling tools like bundle_domain_360 and bundle_kyb_360 through its fusion of data types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('for sales prospecting, KYB, and enrichment agents') and provides a concrete example (domain: stripe.com). While it doesn't explicitly state when not to use it or compare directly with siblings, the context is clear enough for most agents.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the full burden. It mentions cost but does not disclose rate limits, authentication requirements, idempotency, or data freshness. Essential behavioral traits for a data-fetching tool are missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: one line for purpose, one line for inclusions, an example, and cost. Every sentence adds value, and the purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lists the types of data included (market data, spot price, sentiment, etc.), giving a good idea of the output. Cost and example are provided. It lacks format details but is sufficient for an agent to understand the tool's capabilities.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema coverage is 0%, so the description compensates by stating 'for any CoinGecko coin id' and providing an example ('bitcoin'). This adds crucial semantic meaning that the schema alone lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a comprehensive crypto snapshot including market data, spot price, Fear & Greed sentiment, global context, and trending. It specifies it works for any CoinGecko coin id, and contrasts with sibling tools like lookup_coingecko that likely give simpler data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'For trading, research, and portfolio agents,' indicating appropriate use cases. It provides an example call but does not explicitly state when to avoid this tool or suggest alternatives, though the context of one-call comprehensiveness is implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions the data returned (profile + repos) and cost, but does not state if authentication is required, rate limits, or confirm it is read-only. The cost disclosure adds some value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences (plus one for cost) that front-load the purpose, provide an example, and add cost information. No fluff; every sentence serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter, no output schema, and no annotations, the description is fairly complete: it states the purpose, target users, example, and cost. The only gap is lack of output format, but the tool's simplicity makes this acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description implies the 'username' parameter is a GitHub username via the example 'torvalds'. This adds meaning beyond the schema, though an explicit note would be stronger.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides 'Developer/GitHub intelligence: account profile + top repositories for any username.' This specifies the verb (intelligence/data retrieval), resource (GitHub developers), and scope (profile + top repos), distinguishing it from siblings like enrich_github or lookup_github_user.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly targets 'recruiting, dev-rel, and technical-sourcing agents' and provides an example call. While it gives clear context for when to use, it does not mention when not to use or name alternatives among the many sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the cost ($0.005–$0.05 per call) and lists the types of data returned (clearances, recalls, adverse events). However, it does not state if the operation is read-only or describe response format, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences plus a cost line and example. It front-loads the core purpose and avoids redundancy. Every statement adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lists data types (clearances, recalls, adverse events) but does not describe the response format or structure. Without an output schema, more detail on what the agent can expect would improve completeness. The example only shows input.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description compensates with an example call ('name': 'insulin pump') and mentions 'for any device or manufacturer'. This clarifies that the single 'name' parameter accepts a device or manufacturer name. The example adds meaning beyond the schema's empty description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is an 'FDA medical-device risk dossier' providing '510(k) clearances + recalls + adverse events (MAUDE)'. It targets specific agents (medtech, procurement, compliance) and includes an example call. This distinguishes it from siblings like bundle_drug_360 for drugs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains it is for 'any device or manufacturer' and targets 'medtech, procurement, and compliance agents'. The example call clarifies the input format. While it does not explicitly exclude alternatives, the context implies appropriate use without confusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses cost range and output categories, but does not mention read-only nature, error handling, or response format. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded with purpose, then list of components, user groups, example, and cost. No unnecessary sentences, though cost line could be omitted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and multiple data categories, the description provides high-level categories but lacks details on response structure, error cases, or handling of missing data. Adequate for a single-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only parameter is 'name' with 0% schema description. The example call {'name': 'Stripe'} adds real-world context, but no details on format or meaning beyond the obvious.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Specifically states 'Corporate due-diligence dossier in ONE call' and enumerates the exact data types (firmographics, OFAC, risk score, KYB, legal exposure). This clearly defines verb+resource and distinguishes from siblings like bundle_company_360.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Identifies target users: 'For investment, KYB, compliance, and corporate-intelligence agents.' Also implies efficiency with 'in ONE call'. Lacks explicit when-not or alternative comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses what data is returned and cost, but no annotations exist; missing details on rate limits, data freshness, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, example, and cost. No verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers key aspects (returned components, use cases) but omits output structure or error handling; reasonable for a simple query tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds example and context ('for any domain') to the single parameter, but with 0% schema coverage, could specify format or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it bundles WHOIS, DNS, and TLS for any domain, distinguishing it from individual lookup tools like lookup_whois, lookup_dns, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides use cases (security review, vendor due diligence) but lacks explicit guidance on when not to use or alternatives (e.g., for just DNS, use lookup_dns).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the cost and components of the dossier, but it lacks details on output format, rate limits, authorization requirements, or potential limitations. With no annotations present, more behavioral context would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: one sentence defining the tool, an example call, and cost information. Every part adds value, and the key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, so the description should explain what the response contains. It mentions the dossier includes FDA label, approval history, and clinical trials, but does not describe the format or structure. Combined with the one parameter and cost info, it is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has only one parameter (name) with 0% coverage, meaning no schema descriptions. The description compensates by providing the example call with 'name': 'metformin', which clarifies that the parameter accepts a drug name. However, it does not specify formatting or case sensitivity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that this tool provides a 'drug intelligence dossier' comprising FDA label, approval history, and clinical trials for any drug. It also specifies the target audience (pharma, healthcare, research agents) and gives an example, making it distinct from sibling bundle tools like bundle_company_360.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates the tool is for pharmaceutical, healthcare, and research contexts, providing clear domain guidance. However, it does not explicitly mention when not to use it or list alternative tools for specific drug lookups, though the sibling set includes many non-drug tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions cost and hints at enrichment but does not describe side effects, rate limits, or response structure. The cost disclosure is useful but not sufficient for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two sentences, an example, and a cost line. Information is front-loaded with the core purpose, making it efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input (single string parameter) and absence of output schema, the description adequately explains the tool's value and cost. While it does not describe return values, the purpose is clear enough for an agent to decide invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must add meaning. The example 'jane@stripe.com' implies a valid email format, and the phrase 'any email address' clarifies scope. However, no explicit parameter description is given, and the example alone is basic.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs 'deliverability validation + provider/domain enrichment' for email addresses, and lists specific use cases like lead-gen, CRM hygiene, and fraud-screening. This specificity and resource identification make the purpose unmistakable and distinguish it from simpler lookups.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit use cases but does not provide guidance on when to avoid this tool or how it compares to siblings like 'lookup_email_validate'. While the term 'intelligence' implies a more comprehensive analysis, the lack of contrast with alternative tools leaves room for uncertainty.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the tool aggregates data in 'ONE call' and gives cost transparency, but lacks details on read-only nature, error handling, data freshness, or any side effects. The cost disclosure adds value, but overall behavioral traits are minimally addressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: a clear purpose statement, list of included data, use cases, example, and cost. No redundant sentences; information is front-loaded and well-organized. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains what data the bundle includes (sanctions, risk score, etc.), which is sufficient for understanding output scope. However, it does not describe the output structure or how results are formatted, which would aid agent usage. Still, for a single-parameter tool, it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by naming the single parameter 'name' and providing an example (Stripe), clearly indicating it expects a company name. No validation or format details, but the simplicity of one string parameter makes this adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a KYB/counterparty-risk verdict for a company, listing specific components (sanctions, PEP, risk score, etc.) and use cases (onboarding, underwriting, compliance). It distinguishes itself from sibling tools that target other entities (e.g., bundle_crypto_360).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly recommends use cases like customer/vendor onboarding and compliance. It provides an example call and cost range. However, it does not explicitly exclude scenarios where simpler tools might suffice, which would enhance guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Describes data sources and cost ($0.005-$0.05 per call). Does not specify read-only nature or pagination, but pricing and source list add transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, an example, and cost. Efficiently front-loaded with key information, no redundant or verbose phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema or annotations, description covers purpose, data sources, example, and cost. Lacks return format details but sufficient for an agent to decide on usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so description must compensate. It explains the 'term' parameter with context (sector/term) and example ('medical devices'). Does not specify format or allowed values, but provides enough for basic understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves regulatory and legal landscapes for a sector/term, listing specific data sources (Federal Register rulemaking, case-law, FDA device signals, federal grant signals). It distinguishes itself from sibling bundle_* tools by focusing on regulatory domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly targets policy, legal, compliance, and risk-monitoring agents. Provides an example call. Does not explicitly state when not to use, but the specificity and sibling names (e.g., bundle_company_360) imply differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full burden. It discloses cost range and example format, but omits authentication needs, rate limits, response size, pagination, and error handling (e.g., for nonexistent repos). The core behavior is clear but not deeply transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three efficient sentences without redundancy: purpose, example, cost. Front-loaded with the core value proposition, making it quick for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should elaborate on the returned data. 'Repository profile + latest releases' is vague; it lacks field details, structure, or any mention of error responses. Adequate for a simple tool but incomplete relative to its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description only clarifies parameter names via the example call. It does not explain required formats (e.g., exact GitHub username vs. case-insensitive), validation rules, or parameter relationships. The example helps but is insufficient for a no-description schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it provides 'GitHub repo intelligence: repository profile + latest releases' for any owner/repo. This verb-resource pairing distinguishes it from sibling tools like enrich_github, lookup_github, and lookup_github_releases by bundling two data types into one call.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly targets 'dev-tools, security, and OSS-monitoring agents' and provides an example call. While it implies the use case and owner/repo scope, it does not mention when to avoid using it (e.g., for very frequent polling) or suggest alternatives like lookup_github_releases for fewer fields.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must reveal behavioral traits. It mentions cost and includes an example call, but fails to disclose what the tool does beyond bundling multiple checks, such as data sources, error handling, or output structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences covering purpose, usage context, example call, and cost. Every sentence adds necessary information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single required parameter, no output schema, and no annotations, the description is reasonably complete. It specifies input, purpose, and target users, though it omits output details and potential limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one parameter 'name' with 0% description coverage. The description adds value by indicating that 'name' refers to a company name and provides an example. However, it does not specify format or constraints, so it partially compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool provides 'OFAC sanctions screen + entity risk score + KYB/vendor dossier for any company,' which is a specific verb+resource combination. It distinguishes itself from siblings by focusing on risk/compliance bundles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly targets 'compliance, onboarding, and vendor-risk agents,' providing clear context for when to use. Although it does not mention alternatives or when not to use, the usage guidance is clear and sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must disclose behavioral traits. It mentions the type of data returned and cost, but does not disclose data freshness, accuracy, limitations, or potential side effects. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is brief and to the point, including an example and cost information. While not highly structured, it earns its space without repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description adequately explains the output signals and use cases. It does not mention pagination or result limits, but is otherwise complete for its simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds an example keyword ('fintech') and explains what the tool returns. However, it does not describe valid keyword formats or constraints, leaving the agent to infer from context. Partial compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool provides 'sales-intent signals' by industry/keyword, listing specific signal types (funding, contracts, grants, FDA) and use cases (lead-gen, prospecting, sales agents). It includes an example call, making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description targets specific use cases and includes an example, but does not explicitly state when to use versus alternatives or when not to use. Among many sibling tools, no direct differentiation is provided, though the niche is clear enough for an informed agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description should disclose behavioral traits. It only mentions cost and an example, omitting details on rate limits, data freshness, error handling, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, an example call, and cost information. No unnecessary words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description covers purpose and cost. However, it lacks details on output format, pagination, or possible errors, which would be helpful for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage. The description says 'any ticker/CIK' but the parameter is named 'ticker' and does not clarify if CIK is accepted or how to format it. The example uses 'AAPL' but adds limited semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'SEC corporate-financials intelligence: company facts (XBRL financials) + recent SEC filings for any ticker/CIK', specifying the exact data type and source. It distinguishes itself from sibling bundle tools by focusing on SEC data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates usage for 'equity research, investment analysis, and financial agents', providing clear context. However, it does not explicitly mention when not to use or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It mentions cost and gives an example call, but fails to disclose that it is a read-only query, any rate limits, response size, or data freshness. The description is insufficient for safe autonomous invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two main sentences plus a cost note and example. It is front-loaded with the core purpose and no superfluous text. The example call is clear and adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description partially compensates by listing return fields. However, it omits details on response format (list vs. single item), pagination, sorting, and error handling. The tool might require more context for reliable use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'term' is described only by name in the schema (0% coverage). The description adds meaning by stating 'term' can be a condition, drug, or sponsor, and provides an example, but lacks details on query syntax (e.g., partial matches, boolean operators).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool provides clinical trials data from ClinicalTrials.gov for any condition, drug, or sponsor. It lists the return fields (NCT id, title, status, phase) and distinguishes itself from sibling tools which are mostly generic lookups or scrapers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says 'For healthcare, pharma, and research agents,' providing some user guidance, but lacks explicit when-to-use or when-not-to-use guidelines. It also doesn't mention alternatives or constraints like required authority or data limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the data source (Shodan InternetDB) and cost ($0.005–$0.05). It does not mention rate limits, authentication, or read-only nature, but the cost is a key behavioral trait.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no wasted words: purpose, example, cost. Front-loaded with the core value, making it quick for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter lookup tool, the description covers purpose, input example, output details, data source, and cost. No output schema exists, but the description lists return fields, making it complete for decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

One parameter 'ip' with schema coverage at 0%. The description adds meaning by specifying it accepts any IP address, provides an example, and explains the output context (threat intel), compensating for the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides IP threat intelligence including open ports, CVEs, hostnames, and tags from Shodan InternetDB, distinguishing it from siblings like lookup_ip and lookup_ipinfo which likely offer geolocation or basic info.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly targets security, fraud, and abuse-screening agents, and provides an example call. However, it does not explicitly state when not to use this tool versus alternative IP lookups, though the specialized threat-intel focus implies the distinction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavior, but it only mentions cost and a single example. It omits critical details: whether results are real-time, what the response format is (boolean, list of matches, etc.), error conditions, or any rate limits. The phrase 'canonical KYC/KYB gate' implies authority but lacks specifics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences plus an example and cost. Every element earns its place, with the main purpose front-loaded. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the simplicity (one parameter), the description lacks completeness. It does not explain the output (crucial for an action-guiding tool), error handling, or whether all lists are always checked. The cost and example are helpful, but agents need more context to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema coverage, the description partially compensates by providing an example ('name': 'Gazprom') and stating it works for any person/company. However, it does not specify name format, allowed length, or encoding, leaving some ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly defines the tool as a sanctions, PEP, and watchlist screener for any person or company across multiple official lists (OFAC, EU, UK, UN + OpenSanctions). It uses strong action verbs ("screen", "gate") and distinguishes itself from sibling tools (mostly generic lookups/scrapes) with a clear KYC/KYB role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear when-to-use guidance: 'call before onboarding, paying, or transacting with any counterparty'. However, it does not explicitly mention when not to use it or provide alternatives, which would elevate the score to 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description adds cost information and an example call, but does not explicitly state the tool is read-only or disclose rate limits or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences plus an example and cost line. Front-loaded with purpose, no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, description covers purpose, usage context, cost, and example. Missing return format details but not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter (actor_id) with no schema description (0% coverage). Description provides an example value ('apify~instagram-scraper') but no format rules or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get metadata' and resource 'public Apify actor' with specific fields (description, pricing, last build, recent runs), distinguishing it from sibling enrich_* tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases: 'when researching Apify scrapers or comparing actor coverage.' Does not state when not to use, but context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses that the tool uses a headless browser (JS rendering) and returns HTML/screenshot. However, it lacks details on rate limits, authentication, error handling, or potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences plus example and cost. Front-loaded with purpose, then usage guidance, example, and cost. Every sentence earns its place with zero waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given low schema coverage and no output schema, the description adequately explains the tool's function and use case. Could be more complete by specifying the response structure (e.g., fields in the output) but covers key aspects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but description provides an example call: {"url": "https://example.com"}. This adds minimal context beyond the schema's type/required constraint. No further specification on URL format or validation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: 'Headless-browser fetch of a URL with full JS render, returning final HTML and screenshot URL.' It specifies verb, resource, and outputs, distinguishing it from sibling tools like enrich_browsersnap.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'Use when the target page is SPA/JS-rendered and a plain fetch returns empty HTML.' Provides context but does not mention alternatives or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that the screenshot is 'headless' and returns a CDN URL, and it mentions the cost. However, with no annotations, the agent might need more info on whether it handles dynamic content, timeouts, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: one sentence for purpose, one for usage, one example, and one for cost. Every sentence is valuable and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 1-param tool, the description covers the basic input and output (CDN screenshot URL), but lacks details on return format (e.g., JSON or plain URL) and potential error cases. It is adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only provides an example call. It does not explain what the 'url' parameter expects beyond being a URL, leaving the agent to infer from the name. The example is helpful but insufficient for full compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (headless-browser screenshot) and the result (CDN screenshot URL), using a specific verb and resource. However, it does not distinguish from sibling tools like enrich_browserbase, which likely has a similar purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit use cases ('visual snapshot for a report, slide, or QA pipeline'), which is helpful. But it does not mention when not to use or direct users to alternatives like enrich_browserbase.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the cost per call ($0.005–$0.05 USDC on Base) and implies it is a read-only enrichment operation. It does not mention auth requirements, rate limits, or data freshness, but the cost adds useful behavioral context for the agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three short sentences with no fluff: purpose, usage guidance, example, and cost. Every sentence adds value. Well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple single-parameter schema and no output schema, the description is adequate. It explains the tool's purpose, usage context, and cost. However, it could be enhanced with a brief note on the output format or limitations (e.g., no guarantees on field availability).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining the parameter is a 'domain' and provides an example ('{"domain": "stripe.com"}'), which clarifies the expected format beyond the schema's minimal type/title.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('enrich') and resource ('company with metadata'), lists the metadata fields (industry, employee size, founded year, logo, social links), and distinguishes from sibling enrichment tools that target specific platforms (e.g., enrich_github) by focusing on the domain input. The use case for B2B leads, prospects, or competitors clearly differentiates it from other tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use: 'Use whenever you need company context for a B2B lead, prospect, or competitor.' It provides an example call. However, it does not mention when not to use or suggest alternatives (e.g., bundle_company_360 for more comprehensive data).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses the cost range ($0.005–$0.05 USDC on Base) and implies the tool fetches data (read-only). It does not mention any destructive behavior, which is appropriate. The description adds valuable context beyond the tool's basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short (two sentences plus example and cost), front-loaded with purpose, and every sentence adds value. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema), the description covers purpose, usage, example, and cost. It lists the output fields, which helps the agent understand what to expect. Minor omission: does not mention if other fields are returned beyond those listed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It provides an example call with a real username ('torvalds'), which strongly implies the parameter is a GitHub username. This adds meaningful semantics beyond the schema's bare 'string' type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the verb 'enrich' and the resource 'GitHub user profile', listing specific fields like public repo count, followers, hireable flag, top languages, and join date. This clearly differentiates it from sibling tools like lookup_github_user, which likely returns basic profile info.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: 'developer-lead qualification or recruiter sourcing'. This gives clear context for when to use the tool, though it does not specify when not to use it or name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description bears full burden. Describes basic behavior and cost range, but omits details like API calls, rate limits, or that it is read-only. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences plus example and cost. Purpose is front-loaded. Every sentence adds value with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so description should explain return structure. It mentions 'rating distribution' and 'recent review text' but lacks detail on format, limits, or error handling. Leaves gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% so description must compensate. It explains the parameter (Google place_id) and provides an example value, adding significant meaning beyond schema which only has title 'Place Id'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'aggregate' and resource 'Google Maps reviews', and specifies outputs: rating distribution and recent review text. Clearly distinguishes from similar sibling tools like enrich_reviews.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'Use when you already have a Google place_id and need structured review data.' Lacks explicit when-not or alternatives, but provides clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses cost ($0.005–$0.05) and implies real-time data ('live profile context'). However, it does not mention whether the tool is read-only, any authentication needs, rate limits, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with an example and cost. Front-loaded with key output fields. No fluff; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter enrichment tool with no output schema, the description covers the return fields and use cases. It lacks error handling or rate limit info, but the context signals show low complexity, making it fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no description in schema). The description adds an example call with 'username': 'natgeo', clarifying the parameter format. But it doesn't specify whether the username should include '@', case sensitivity, or validation rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Enrich an Instagram profile' and lists specific data points (follower count, bio, business category, etc.). It distinguishes from siblings by targeting Instagram specifically, and the listed fields are unique to enrichment tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear use cases: 'qualify an Instagram lead, score a creator for an influencer campaign, or pull live profile context for a prospect'. Does not mention when not to use or alternatives, but the context is specific enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions cost and provides an example, but does not disclose potential rate limits, authentication requirements, or error behavior for invalid slugs. It implies a read-only enrichment operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise: two sentences plus an example and cost. Every sentence earns its place, with no fluff. The purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one required parameter, no output schema), the description is complete. It explains what the tool returns, how to use it, and its cost, covering all necessary aspects for an agent to select and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter, 'slug', is explained as 'the part after /in/' in the description, which adds value beyond the input schema's title. The schema has 0% description coverage, so the description compensates well.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'enrich' and the resource 'public LinkedIn profile', specifying the output data (headline, current company, experience snapshot) and the input key (vanity slug). It distinguishes itself from sibling enrich tools by focusing on LinkedIn and using a slug.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use for B2B lead enrichment when you only have a LinkedIn URL', giving a clear use case. It does not state when not to use it, but the context of sibling tools makes it easy to infer, so it's clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must cover behavioral traits. It mentions cost range and aggregation of reviews, but lacks details on rate limits, authentication, or output structure. The cost disclosure adds some transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with purpose and example. Every sentence adds value, though the cost line could be seen as secondary. Still, it's efficient and readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with one parameter and no output schema, but the description does not fully explain the query parameter's format or the structure of the returned data. It mentions aggregated fields but lacks detail on formatting or limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only hints at the query parameter via an example ('blue bottle coffee oakland'). It does not explain the format or required specificity (e.g., business name + location). Minimal added value beyond the schema's name and type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it aggregates Google Maps reviews (rating, count, snippets) for local businesses, with specific use cases like local-SEO and competitor monitoring. This distinguishes it from sibling tools like enrich_googlereviews.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

An example call is provided, implying usage, but no explicit guidance on when to use this vs. alternatives or when not to use it. Sibling tools are numerous but no comparison is made.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses cost per call ($0.005–$0.05 USDC) and provides an example, which adds value. However, no annotations exist, so description should explicitly state read-only nature, rate limits, or failure handling; it omits these, limiting transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise: one sentence for purpose, one sentence for use cases, one example, and cost. Front-loaded and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Lists returned fields (listeners, followers, tracks, genres) and includes cost, which helps agents. No output schema, so some details missing (e.g., pagination, error responses), but adequate for a simple enrichment tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, description must compensate. The example shows a valid artist_id value, but no formal explanation of format, source, or required pattern. Suffices for basic use but could confuse new users.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool enriches a Spotify artist profile with specific data fields (monthly listeners, follower count, etc.), distinguishing it from other enrich_ tools by naming the platform and data types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases: music marketing, A&R research, and playlist-pitching workflows. Does not offer when-not-to-use or alternative tools, but context is sufficient for typical scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden. It lists returned fields and cost, but does not explicitly state it is a read-only operation or disclose any side effects. The example call and cost info add some transparency, but more details on behavior would improve this score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—three sentences that cover purpose, usage, example, and cost. Every sentence adds value with no redundancy or extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of output schema, the description sufficiently explains what the tool returns (follower count, bio, verified status) and its cross-platform context. It lacks details on error handling or rate limits, but for a simple enrichment tool this is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so the description compensates by providing an example call with 'username': 'zuck'. This clarifies that the parameter expects a Threads username, adding meaning beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it enriches a Threads profile with specific fields (follower count, bio, verified status), and highlights its use for cross-platform social-presence checks, effectively distinguishing it from sibling tools like enrich_instagram or enrich_x.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use for cross-platform social-presence checks', providing clear context. It does not mention when not to use or give alternatives, but the tool name and siblings make the platform-specific intent obvious.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions cost and that it pulls live data, but lacks details on prerequisites, authentication, rate limits, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences plus an example, front-loaded with action and data. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple schema and no output schema, the description provides a good overview of return data (follower count, etc.) and cost. However, it does not specify output format or possible limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the single 'username' parameter is explained through the example and context. The description adds value beyond the schema by indicating the expected use.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it enriches a TikTok profile and lists specific data points (follower count, total likes, bio, verified status, recent post stats). This distinguishes it from sibling tools like enrich_instagram.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a specific use case ('qualify a TikTok creator for paid partnerships') and an example call. However, it does not explicitly state when not to use it versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It does not disclose whether the tool is read-only, any authentication requirements, rate limits, or side effects. It only mentions cost, which is useful but insufficient for a 1-param tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two sentences plus an example and cost. It is front-loaded with purpose and usage. No fluff, but it could be slightly more structured (e.g., separate sections).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lists the returned fields (follower count, bio, etc.), partially compensating for no output schema. However, it omits error handling, data freshness, and behavioral details. Given low complexity, it is moderately complete but has gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and there is only one parameter ('username'). The description does not add meaning beyond the schema; it merely gives an example with 'elonmusk'. It does not specify format, constraints, or allowed values, relying entirely on the example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: enrich an X/Twitter profile by providing specific fields (follower count, bio, etc.). It uses a specific verb ('Enrich') and resource ('X/Twitter profile'), and it is distinct from sibling tools that enrich other platforms (e.g., enrich_github, enrich_instagram).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use when you need live X account context for lead research or influencer scoring,' providing clear context. However, it does not explicitly state when not to use or list alternatives, though sibling tool names imply different platforms.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the cost per call ($0.005–$0.05 USDC), but does not mention read-only nature, rate limits, response format, error handling, or any side effects. The cost information adds some transparency, but significant behavioral details are omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus an example and cost info. It is front-loaded with the core purpose, every sentence adds value, and the example clarifies usage. No redundant or vague phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter and no output schema or annotations, the description covers the core behavior, supported file types, example usage, and cost. It gives use cases but lacks details about the response structure or error behavior. Given the simplicity, it is nearly complete but could mention return format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides only a parameter name ('query_string') with 0% description coverage. The description adds crucial meaning: it specifies that the parameter should contain a URL, lists supported file types (PDF/PNG/JPG), and provides an exact example call format. This fully compensates for the schema's lack of detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Extract structured data' and specifies the resource (document URL) with supported formats (PDF/PNG/JPG). It distinguishes from sibling tools by focusing on document AI tasks like invoice processing and form parsing, which are absent in other tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains how to pass the URL (via query_string with 'url=' prefix) and lists use cases (invoice processing, form parsing). However, it does not explicitly state when not to use this tool or mention alternative tools for similar tasks, missing exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden for behavioral disclosure. It adds the trait that the tool is 'Free,' and implies it is a read-only operation returning text. It could be improved by noting that no side effects occur, but for a zero-parameter tool, this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that front-loads the action and resource, followed by a list of supported clients. The word 'Free' adds value without bloat. Every part earns its place, with zero wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there are no parameters and no output schema, the description fully explains what the tool returns and for which clients. An agent can invoke this tool with confidence, knowing what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline score is 4 per guidelines. The description does not need to add parameter information since there are none, and the schema coverage is 100% by default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns ready-to-paste configuration snippets for installing the MCP server in specific platforms. The verb 'return' and the specific resource ('configuration snippets for installing this MCP server') make the purpose unambiguous, and it distinguishes itself from sibling tools that perform data lookups or scrapes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the user when to use this tool: when they need installation snippets for the listed clients. Although it does not mention when not to use it or provide alternatives, the context of sibling tools (none similar) makes the usage guideline clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but the description adequately discloses free usage, no wallet requirement, and the nature of output (paid endpoints with prices and status). Could add details about output format but not required for this simple tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences efficiently convey purpose and usage. No unnecessary words, front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, no output schema, and straightforward functionality, the description fully covers what an agent needs to know to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters, so baseline 4 per guidelines. Schema coverage is 100%, and description adds no redundant information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all paid endpoints with prices and live status, and distinguishes it from sibling tools by noting it's a discovery entry point. The verb 'list' and resource 'endpoints' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises 'Use this first to discover what tools are available,' providing clear context for when to invoke. Does not mention when not to use, but for a discovery tool, this is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose all behavioral traits. It implies a read-only operation but does not clarify if the parameter affects the randomness, rate limits, or any other side effects. The cost mention is not behavioral.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief with two sentences plus cost info. It is front-loaded and each part serves a purpose, though the cost line could be integrated into a usage note.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and an unexplained parameter, the description is incomplete. The agent needs to know the return format and how the parameter influences the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain the 'query_string' parameter at all. The parameter's default empty string suggests it might be optional, but its effect is unknown.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a random piece of advice, which is a specific verb+resource. While it distinguishes from many sibling lookup tools, it does not explicitly differentiate from similar tools like lookup_joke or lookup_random_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly recommends using this for content-fill or personal-assistant agents, providing clear context. However, it lacks when-not-to-use guidance or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full burden. It discloses cost and the calculation nature, but does not mention edge cases (e.g., future dates, leap years) or confirm idempotency. Basic transparency is present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences plus example and cost: each element adds value. Purpose is first, then usage context, then example, then cost. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage, and cost, but lacks output format details (the example only shows input) and does not address potential errors. For a simple tool, it is adequate but could be more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds the required format 'YYYY-MM-DD' and an example call. This compensates for the missing schema documentation, making the parameter semantics clear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Calculate' and resource 'age from birthdate', with explicit output in years/months/days. The example and cost further clarify its function. It distinguishes from siblings by specifying a unique calculation (age) rather than generic lookups.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use for HR and registration agents', providing context. It does not mention when not to use or alternatives, but the use case is clear enough among many lookup siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It mentions cost and gives an example query, but does not disclose response format, error handling, or rate limits. Minimal but acceptable for a simple lookup tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences plus example and cost note. Every sentence adds value, no redundancy, purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should explain return values, but it does not. It covers purpose, usage context, and cost, but lacks parameter semantics and return format, making it only partially complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage and only one parameter 'query' with no explanation. The description does not clarify acceptable formats, case sensitivity, or expected input beyond the example 'frieren'. Insufficient for complete understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description states 'Search AniList for anime/manga metadata' with a specific verb and resource, and adds context for use in anime-recommendation and otaku-content agents, effectively distinguishing it from sibling lookup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit context ('Use for anime-recommendation and otaku-content agents') but lacks explicit when-not-to-use or alternative tools, though the purpose is clear enough to imply scope.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does not disclose behavior such as what happens on no results, pagination, rate limits, or response format beyond a brief list. The cost mention is useful but insufficient for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (four lines), front-loaded with purpose, followed by an example and cost. Every sentence is informative, and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lists the fields returned (title, authors, abstract, PDF link) but lacks details like result count, ordering, or recency. It is adequate but could mention that results are limited to recent papers and sorted by date.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds only an example call. It does not explain query syntax, operators, or formatting tips. The single parameter 'query' is minimally described beyond its name and example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Search arXiv for recent papers matching a query' and lists the fields returned (title, authors, abstract, PDF link). This distinguishes it from sibling tools like lookup_wikipedia or lookup_weather, which serve different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use for ML/AI research agents and literature review', providing a clear context of use. However, it does not mention when not to use it or compare to alternatives among the many sibling lookup tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses cost range ($0.005–$0.05 USDC per call) but lacks other behavioral traits such as read-only hint, rate limits, or response size. Since no annotations are provided, the description partially compensates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise: two sentences plus example and cost. Front-loaded with purpose, no fluff, every sentence provides value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given simplicity (one param, no output schema), the description covers purpose, usage context, example, and cost. Could mention output format explicitly, but the fields are already listed. Adequate for agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The 'asn' parameter has no schema description (0% coverage). The description only implies the input via example 'AS15169' but does not explicitly state the required format (e.g., with or without 'AS' prefix). Limited added meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Get ASN metadata' with specific fields (org, country, CIDR ranges), distinguishing it from sibling lookup tools like lookup_ip or lookup_whois.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends use for 'network-research and threat-intel agents', giving clear context. However, it does not directly contrast with sibling tools or specify when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the cost but does not explicitly state if the operation is read-only or destructive. While base64 encoding is inherently safe, the description lacks explicit behavioral safeguards or side-effect disclosures.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences plus an example and cost note. It is front-loaded with the core purpose, then provides usage details. Every sentence adds value without repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool, the description covers the operation, parameter format, example call, and cost. It does not include error cases or return value details, but the example and cost provide sufficient context for an agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% coverage for parameter description. The description compensates by explaining the expected format of the query_string parameter: 'Pass ?text=...&op=encode|decode as query.' It provides a concrete example, adding meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Encode or decode base64.' It uses a specific verb+resource and leaves no ambiguity. It distinguishes itself from siblings like lookup_hash or lookup_url_encode by explicitly naming base64 encoding/decoding.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes 'Use for data-format agents,' which implies a context but provides no explicit guidance on when to use this tool versus alternatives (e.g., lookup_hash, lookup_url_encode). There are no when-not-to-use instructions or alternative tool references.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided. The description discloses cost per call, which is helpful, but lacks details about auth, rate limits, or side effects. The word 'search' implies non-destructive read, but more transparency would be beneficial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise: two sentences plus cost line. Purpose is front-loaded. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, it lists returned fields adequately. For a simple lookup, it covers essential info. Could mention if results are limited or paginated, but not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description carries burden. It adds meaning by specifying that the query relates to beer metadata fields and provides an example. This goes beyond the schema's bare 'string' type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the verb 'Search' and the resource 'craft-beer metadata', listing specific fields (name, brewery, abv, ibu, style). This distinguishes it from other lookup tools by domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It mentions 'Use for hospitality and food agents', providing a clear usage context. However, it does not explicitly state when not to use or compare to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does not disclose any behavioral traits (e.g., read-only, no side effects, rate limits, or authentication needs). The cost hint is helpful but insufficient for behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences, an example, and cost. Every part serves a purpose, and the key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter lookup tool without output schema, the description covers purpose, use case, example, and cost. It lacks return format details but is sufficient for the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'handle' has 0% schema description coverage, but the description includes an example call ('jay.bsky.team'), which partially clarifies the parameter's format. However, no explicit semantics or constraints are added beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Get' and resource 'Bluesky profile', and lists exact fields (followers, bio, post count, avatar), clearly distinguishing it from sibling lookup_* tools for other platforms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use for emerging-platform creator research', providing clear context. However, no exclusions or when-not-to-use guidance are given, but this is acceptable for a simple tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses cost ($0.005–$0.05 per call), which is valuable, but does not mention any other behavioral traits such as rate limits, required authentication, or whether the operation is read-only. The cost disclosure adds some transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the main action and use cases, followed by an example and cost. Every sentence adds value, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has a single parameter and no output schema, the description is fairly complete: it explains what it does, when to use it, provides an example, and mentions cost. It could optionally describe the output structure, but not essential for this simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'domain' has 0% schema description coverage. The description provides an example call with 'stripe.com', which clarifies usage, but does not specify expected format (e.g., with or without protocol). The example compensates partially.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the verb 'Detect' and the resource 'tech stack of a website', listing specific categories (frameworks, analytics, CMS, hosting). It distinguishes itself from sibling tools like lookup_domainage or enrich_company by focusing on technology detection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states when to use: 'for competitive analysis and lead enrichment'. It does not explicitly state when not to use or mention alternatives, but the specific use cases provide sufficient guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must disclose behaviors. It mentions cost but does not cover rate limits, authentication, or error handling. Minimal transparency beyond cost.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is very concise: three sentences plus example and cost. Every sentence adds value, front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Lists some return fields but no output schema exists to fill gaps. Does not specify full output structure or error scenarios. Adequate for simple lookup but incomplete for complex return.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and description only provides an example (bitcoin) without explaining the format, case sensitivity, or allowed values for coin_id. Insufficient guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it retrieves detailed CoinGecko metadata for a coin, listing specific fields. However, it does not differentiate from sibling tools like lookup_crypto, which may have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an example call and mentions use for crypto-research agents, but lacks explicit guidance on when not to use or alternatives. Cost information helps with budgeting.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses cost ($0.005–$0.05 per call) and indicates it's a calculation (read-only) tool. This is important behavioral context, though it could mention error conditions or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences, front-loaded with purpose, then input format, then cost. No unnecessary words, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 1-parameter tool without output schema, the description covers purpose, input, and cost. Missing details about the output format (e.g., ratio number) but still adequate for the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds meaning by explaining the parameter format ('Pass 'FG/BG' (no #)') and providing an example. However, it doesn't explicitly state that FG is foreground and BG is background, which would be helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Calculate', the resource 'WCAG color-contrast ratio', and specifies input format 'two hex colors in FG/BG format without #'. It distinguishes from siblings as a unique color contrast tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use for accessibility and design agents' and provides an example call, giving clear context for when to use. However, it lacks explicit when-not or alternative tool guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must cover behavior. It mentions cost per call but does not explicitly state that the tool is read-only or non-destructive. The verb 'Generate' implies computation without side effects, but more transparency (e.g., 'Read-only operation') would improve the score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with three essential sentences and an example. It front-loads the purpose. However, the cost mention could be integrated into a single line, but overall it remains efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description does not explain the return format (e.g., list of hex codes). For a simple one-parameter tool, it is adequate but could be more complete by hinting at the output structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'seed_hex' has no schema description (0% coverage). The description adds meaning by specifying it is a hex code and providing an example value, significantly aiding the agent in correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Generate' and the resource 'complementary color palette' from a seed hex code. It distinguishes from siblings like lookup_color_contrast by specifying its use for design agents and theme generators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use for design agents and theme generators,' indicating when to use. It lacks explicit when-not-to-use or alternative tools, but the provided use case is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must cover behavioral traits. It discloses cost ($0.005–$0.05 per call) and includes an example call, but does not mention idempotency, rate limits, data freshness, or error handling. For a simple read operation, this is adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise: one sentence for purpose, one line for use cases, an example call, and cost. Every element is functional and front-loaded. No redundant text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, no output schema, no annotations), the description covers the essential aspects: purpose, example, and cost. It could mention possible return values or limitations, but overall it is sufficiently complete for an AI agent to select and use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes the single parameter 'country' with no description, and schema description coverage is 0%. The description provides an example value ('japan') but does not clarify accepted formats (e.g., full name, ISO codes, case sensitivity). This adds some meaning but leaves ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves country metadata including capital, population, currency, languages, and flag. It specifies a concrete verb ('Get') and resource ('country metadata'), distinguishing it from sibling lookup tools that target different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description suggests use cases: 'localization, finance, and travel agents.' This provides context but does not explicitly state when not to use it or compare with alternatives like `lookup_geocode` or `lookup_currency_historical`. Thus, usage guidance is clear but lacks exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses cost ($0.005–$0.05 per call) and provides an example call, but lacks details on safety (read-only), rate limits, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, example, cost. Every sentence adds value. Front-loaded with key information. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no output schema and no annotations, the description is adequate but incomplete. It mentions returned fields (version, downloads, repo) but doesn't detail the output structure or error states.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, so the description must compensate. It provides an example ('pkg: tokio') and context ('Rust crate'), adding meaning beyond the bare schema. Could further clarify expected format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Get crates.io package metadata for a Rust crate (latest version, downloads, repo). Use for Rust-dependency research.' This specifies the verb, resource, and scope, distinguishing it from other package lookup tools like lookup_npm or lookup_pypi.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a clear use case: 'Use for Rust-dependency research.' While it doesn't explicitly state when not to use or suggest alternatives, the context is sufficient for the agent to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. It adds the cost per call ($0.005-$0.05 USDC on Base) and clarifies it is not a fraud check, providing useful behavioral context. However, it does not describe side effects, authentication needs, or response format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences covering purpose, usage, and cost. Front-loaded with the key action. No unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (one parameter, no output schema), the description covers purpose, parameter format, usage boundary, and cost. However, it lacks details about the return value (e.g., isValid, network name), which an agent needs to interpret the result. This gap reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates fully by explaining the query string format: 'Pass ?number=... as query' and providing an example 'number=4111111111111111'. This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool validates a credit-card number via Luhn algorithm and detects the network. It specifies the verb (validate/detect) and resource (credit card number). While it distinguishes from potential fraud-check tools, it does not explicitly differentiate from other lookup_* tools, but the uniqueness is implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use for fintech UX agents (NOT a fraud check).' This tells when and when not to use it, with clear context. No alternatives mentioned but the exclusion is valuable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the data source (CoinGecko) and cost per call, but lacks details on authentication requirements, rate limits, or whether the operation is read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences plus example and cost line, with no unnecessary words. The main action is front-loaded, making it easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description covers purpose, source, use cases, and cost. However, it does not specify the output structure (e.g., currency unit, exact fields) or error behavior, leaving some gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It provides an example call with {'symbol': 'btc'} and lists example tokens (BTC, ETH, SOL), but does not specify case sensitivity or whether full names are accepted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves live crypto price and 24h change for a given symbol, sourced from CoinGecko. It uses specific verbs and resources, but does not differentiate from the sibling tool 'lookup_coingecko', which may offer broader data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It suggests use cases like portfolio agents and trading bots, and provides a cost range, but does not specify when not to use this tool or mention alternatives like the sibling 'lookup_coingecko' for more comprehensive data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Describes cost ($0.005–$0.05), implying a paid API call. 'Get' implies read-only, no destructive effects stated. Cost disclosure adds transparency beyond what annotations would cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences plus example and cost. Front-loaded purpose, then format, then use case, then example. No fluff. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, description covers purpose, parameter format, use case, and cost. Could optionally describe return value structure, but not required given simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter with 0% schema description coverage. Description compensates by explaining format: 'Pass base/target/date (YYYY-MM-DD)' and provides example 'USD/EUR/2024-01-15'. This clarifies the expected structure fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Get a historical FX rate', a specific verb+resource. Distinguishes from sibling tools like lookup_exchange or lookup_crypto by specifying 'historical' and FX. The example reinforces intent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly suggests use cases: 'accounting backfill or historical-analysis agents'. Does not mention when not to use or alternatives, but provides clear context for appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full weight. It discloses cost ($0.005–$0.05 USDC per call) and provides an example call, but does not describe error handling, rate limits, or behavior for missing CVEs. The pricing information adds value, but more behavioral context would be beneficial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with three clear components: purpose, example, and cost. It is front-loaded with the main purpose and each sentence adds unique value. Could be slightly more structured, but it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers purpose, an example, and cost. It lists the data fields returned, which is helpful. However, it does not address error scenarios or the exact format of the response, leaving some gaps for a complete understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal semantic value beyond the input schema. It includes an example format for cve_id (e.g., 'CVE-2021-44228'), which helps clarify the expected format. However, with 0% schema coverage and only one parameter, more details (e.g., validation rules) would improve scoring.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Look up', the resource 'CVE', and specifies the data returned (description, CVSS, references, affected products). It also mentions the target audience (security-monitoring agents), which distinguishes it from other lookup tools among siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for security-monitoring agents but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. While context is given, it lacks clear guidance on when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses a cost range ($0.005–$0.05 USDC per call) and provides an example invocation. It does not detail rate limits, error handling, or idempotency, but for a simple read-only lookup, the information is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences: purpose, example, cost. It is front-loaded with the primary function, contains no redundancy, and every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single parameter, no output schema, and no annotations, the description covers the essential purpose and provides an example. It mentions output includes 'meanings, examples, phonetics', which is adequate for a simple dictionary tool. Slightly more detail on output format could improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no description in schema), so description must compensate. It specifies the parameter is an 'English word' and gives an example ('ephemeral'), adding meaning beyond the schema's type 'string'. This adequately clarifies the parameter's role and format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get a dictionary definition for an English word (meanings, examples, phonetics)', specifying the verb 'Get', the resource 'dictionary definition', and the scope 'English word'. This distinguishes it from sibling lookup_ tools that target different domains (e.g., weather, crypto).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises 'Use for writing and language agents', providing clear context on when to employ the tool. However, it does not explicitly mention when not to use it or suggest alternative tools, though no direct competitor exists among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the cost range ($0.005–$0.05 USDC) and provides an example, but it does not mention rate limits, error handling, or what happens if the domain is invalid or unreachable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with three sentences plus a cost line, front-loading the main purpose. No redundant information, and the example call aids understanding without unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple lookup tool with one parameter and no output schema, the description covers purpose, supported record types, use cases, example, and cost. It lacks details about the return format or potential errors, but is largely sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds value by explaining the domain parameter through context and example. It clarifies that the domain should be provided without protocol, but does not explicitly state format requirements.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it resolves DNS records for a domain, listing specific record types (A, AAAA, MX, TXT, NS). It also provides use cases for security audits, email-deliverability checks, and infra discovery, which distinguishes it from other lookup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly mentions when to use the tool (security audits, email-deliverability, infra discovery) and gives an example call. However, it does not specify when not to use it or mention alternatives like lookup_mxrecords for MX-only queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses cost and indicates the type of data returned, but does not mention rate limits, authentication requirements, or error behavior (e.g., invalid image name).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences: purpose, example, cost. Front-loaded with purpose, no unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter and no output schema, the description lists the key return fields (last push, pull count, tags, size) and provides an example. It lacks details on error handling or edge cases, but is mostly complete for a simple lookup.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, meaning the description must compensate. It provides an example ('library/postgres') that illustrates the format, but does not explain whether 'library/' is required or how to specify private images. This is adequate but not thorough.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Get Docker Hub image metadata' with specific fields (last push, pull count, tags, size) and use case (container audits, supply-chain research). It clearly distinguishes from sibling lookup tools like lookup_pypi or lookup_npm.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a concrete example call and cost range, giving context for when to use. However, it does not explicitly mention when not to use this tool or alternative tools like scrape_dockerhub, which could be relevant.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must disclose behaviors. It mentions cost per call, but does not disclose side effects, speed, rate limits, or response format beyond 'info + image'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise with three short sentences, an example, and cost info. No wasted words, though could be better structured with sections.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema or annotations. Description provides basic purpose and cost, but lacks details on return format, limitations, or data freshness. Adequate but not rich.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Single required parameter 'breed' with no schema description (0% coverage). Description provides an example ('shiba') but no valid values, case sensitivity, or formatting rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get info + image for a dog breed' with a verb and resource. It is distinct from sibling lookup_* tools by focusing on dog breeds, but does not explicitly differentiate from them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Only suggests 'Use for pet content agents.' No guidance on when not to use or alternatives among many similar lookup tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses monetary cost and implies a read-only operation (no mention of side effects). However, it omits details like rate limits, whether the domain must exist, or the exact return format (e.g., only age or also creation date).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: three sentences covering purpose, example, and cost. It is front-loaded with the core action (Get a domain's age) and immediately adds context with use cases and an example, making it efficient and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers basic purpose and cost. However, it lacks information about error handling (e.g., for invalid domains), the exact data returned (e.g., both creation date and age in years), and any limitations. It is minimally complete but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the domain parameter. The description provides an example call ({"domain": "google.com"}) which illustrates usage but does not explain the expected format (e.g., full domain without protocol). The schema defines a required string, but the description adds minimal semantics beyond the example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get a domain's age (creation date, age in years).' It explicitly mentions use cases like trust scoring and SEO research. While it distinguishes from siblings by focusing solely on domain age, it does not explicitly differentiate from lookup_whois which may also provide age-related data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description suggests use for 'trust scoring and SEO research,' providing reasonable context. However, it does not specify when to avoid this tool, mention prerequisites (e.g., domain must be registered), or contrast with alternatives like lookup_whois for more comprehensive domain data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses key behaviors: syntax validation and MX-record check. Also includes cost information ($0.005–$0.05 USDC). Since no annotations are provided, the description effectively communicates the tool's non-destructive nature and operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences covering purpose, usage scenario, and cost. Front-loaded with the core action, no unnecessary words. Achieves maximum clarity with minimal length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the tool's functionality, use case, example, and cost. However, does not describe the return value format (e.g., boolean or detailed error), which would be helpful given the lack of output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has only one parameter ('email') with 0% schema description coverage. The description compensates by providing an example call and explaining what validation entails, adding meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Validate' and resource 'email address' with specific scope 'syntax + MX-record check'. It distinguishes from siblings like lookup_mxrecords by combining both checks, and no other sibling tool focuses on email validation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a clear use case: 'Use for lead-list cleaning before sending cold email.' This guides the agent's decision. However, it does not explicitly mention when not to use or suggest alternatives, which would make it stronger.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits. It mentions cost and provides an example but does not state that the tool is read-only, non-destructive, or any other behavioral aspects such as rate limits or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with three sentences: purpose and output, usage context, and example with cost. It is front-loaded with essential information, and every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description covers the core functionality, output format, example, and cost. It is nearly complete, though it could mention any constraints or error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description includes an example call with the query parameter, which helps clarify the parameter usage. However, it does not describe the parameter's meaning beyond the schema name, missing the opportunity to add full semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search', the resource 'emojis', and the output format 'unicode + shortcode + category'. It also specifies the use case for content-generation agents, differentiating it from other lookup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a context for when to use ('Use for content-generation agents') but lacks explicit when-not-to-use or alternatives. It gives an example call but no guidance on choosing this tool over similar tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Discloses cost per call ($0.005–$0.05 USDC) and real-time nature, but lacks details on idempotency, error handling, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, use cases, example, and cost. No fluff, all information earns its place. Front-loaded with key action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool with no output schema, description covers parameters, use cases, and cost. Missing return format, but acceptable given simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description explains parameters: 3-letter ISO codes, base→target direction, and provides an example. Adds meaning beyond bare property names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Get a live FX rate' with specific verb and resource, and mentions ISO currency codes. Distinguishes from many sibling lookup tools by focusing on foreign exchange.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides use cases like 'pricing localization, accounting, or finance agents.' Implies real-time use but does not explicitly contrast with sibling 'lookup_currency_historical'. Slight gap in when-not-to-use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Given no annotations, the description adequately covers data source, return fields, and cost. It could mention rate limits or data freshness but is sufficiently transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two sentences plus example and cost. All information is relevant and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description covers purpose, input format, return data, and cost, making it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description adds meaning by specifying 'UPC/EAN barcode' and providing an example, compensating fully for the schema's lack of detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it looks up food products by barcode using Open Food Facts, listing return fields (nutrition, ingredients, brand). This clearly distinguishes it from other lookup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description suggests use cases ('grocery, dietary, or scanning agents') and provides an example call, but does not explicitly mention when not to use it or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description discloses cost ($0.005–$0.05 USDC) but does not mention rate limits, authentication requirements, or side effects. For a geocoding lookup, it is likely read-only, but the description does not explicitly state safety or behavioral traits beyond cost.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: three sentences covering purpose, usage hint, example, and cost. No redundant or unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequately covers purpose and usage, but lacks details about response format (lat/lon structure) and error handling. Given no output schema, the description could be more complete to help the agent parse results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage; the description adds critical context by explaining the query format (including 'q=' prefix) and providing an example. This compensates for the schema's lack of parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Forward-geocode an address to lat/lon' with a specific verb and resource. Distinguishes from sibling 'lookup_reverse_geocode' and provides use case for mapping and logistics agents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage hint 'Pass ?q=... as query' and an example call. Mentions intended use for mapping and logistics, but lacks explicit when-not-to-use or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It discloses cost ($0.005–$0.05 USDC per call) and includes an example call. However, it omits details like rate limits, data freshness, or repo visibility scope, but the provided info is valuable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences covering purpose, use cases, example, and cost. It is front-loaded with the core function, no redundant information, and every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple lookup tool with two parameters and no output schema, the description is sufficiently complete. It states the return fields, provides a usage example, and includes cost. Could mention that output is a JSON object, but not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description should elaborate on parameters. It only provides an example ({'owner': 'torvalds', 'repo': 'linux'}), which implicitly clarifies the meaning. The parameter names are self-explanatory, so minimal extra semantics are needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the verb 'Get' and the resource 'GitHub repo metadata' with concrete fields (stars, language, license, dates, default branch). It clearly distinguishes from the sibling 'enrich_github' by focusing on core metadata retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: 'OSS research, dependency-risk scoring, or maintainer outreach'. While it does not explicitly state when not to use, the context given is clear and helps the agent decide appropriateness.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.