DeriveAI x402 APIs

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

With no annotations provided, the description carries the burden, and it rises well above the minimum: it details what the tool returns (sentiment, confidence score, dialect, keywords) and the language support. It doesn't specify edge-case handling (e.g., non-Arabic input, long text errors), but it's transparent about its core behavior and limitations, such as the 10,000-character limit.

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 tightly-written sentences cover the what, why, and output format without any fluff or repetition. Every clause adds value, from the list of dialects to the differentiator about LLM accuracy. This is excellent use of space and very easy to parse quickly.

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 a single textual parameter and no output schema, the description is impressively complete—it covers the return values (sentiment, confidence, dialect, keywords) and sets expectations for the tool's specialization. It could add detail on error handling or non-Arabic input behavior, but given the low complexity, this is more than sufficient.

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 for the single parameter 'text' is 100%, and the parameter description ('Arabic text to analyze') aligns directly with the tool's purpose. The description adds value by specifying the max length and expected language, making the parameter's meaning even clearer than 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 uses concrete verbs and nouns — 'Analyze sentiment in Arabic text' — and clearly enumerates its supported dialects (Gulf/Khaleeji, Egyptian, Levantine, MSA). It distinguishes itself from generic LLMs by targeting a specific niche (45% accuracy gap on Gulf dialect), which sets it apart from sibling 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 implies when to use it (any Arabic sentiment analysis, especially for dialects) and contrasts with generic models, but doesn't explicitly name alternative tools or say 'use X instead for language detection' (e.g., detect_language). The context is clear enough for an agent to make a good choice, but there's room to be more explicit about 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?

No annotations exist, so the description carries the full burden of disclosure. It explains the transformation rules, includes number-to-letter mappings (3=ع, 7=ح, etc.), and indicates dialect focus. This goes beyond a minimal description by explaining how the tool behaves, though it doesn't address edge cases like handling of proper nouns or mixed-script input.

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 exceptionally concise—two sentences—yet packs in the action, input/output examples, dialect note, and key mappings. Every phrase adds value with no redundancy or fluff. The structure front-loads the core purpose and follows with clarifying details.

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 a single parameter and no output schema, this description is highly complete. It covers the tool's purpose, provides input/output examples, specifies dialect optimization, and lists common number mappings. An agent could confidently invoke this tool based on the description alone.

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 already provides 100% coverage by describing the 'text' parameter with examples and a 5,000-character limit. The description adds no additional parameter-specific information beyond what the schema provides, aligning with the baseline score of 3 for high 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 uses a specific verb ('Convert'), identifies the exact resource (Arabizi to Arabic script), and includes concrete examples ('7abibi', 'حبيبي'). This clearly distinguishes the tool's function from any sibling tool, even without naming alternatives, 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?

The description provides clear context by specifying 'Gulf dialect optimized' and gives examples of input types (numbers substituting Arabic letters). While it doesn't explicitly name alternatives or state when not to use the tool, the context is sufficient for an agent to infer appropriate usage. No exclusions are mentioned, aligning with a score of 4.

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 discloses the checks performed and the scored report output, but doesn't explicitly mention the read-only nature or any limitations (e.g., real-time data, platform variability). Adequate but not rich.

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 concise sentences, no filler. The first sentence labels the tool, the second enumerates the full scope and output format.

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 one simple parameter and no output schema, the description sufficiently explains what the tool does and returns. It could provide more detail about the report structure, but the high-level scope is clear.

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 'name' is fully described in the schema ('Brand/business name to check'). The description does not add further meaning, so the baseline 3 applies.

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 specific verbs and resources: 'Checks domain availability across 12 TLDs, social handle availability on 15+ platforms, trademark conflicts, and alternative name suggestions.' This clearly distinguishes it from sibling tools like domain_enrich or username_osint and specifies the output as a scored report (0-100).

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 context: when assessing brand availability. It doesn't explicitly state when not to use it or name alternatives, but the detailed scope gives sufficient context for selecting this 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 the burden. It discloses the cost ($1.00) and the nature of the checks but does not mention potential side effects, rate limits, or any limitations of the checks (e.g., data freshness). The description gives a good overview but lacks explicit behavioral 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?

The description is information-dense, listing many checks in a compact list format. It avoids unnecessary fluff, but the list is somewhat long. Still, it serves the purpose of conveying the tool's comprehensive nature efficiently.

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 broad scope, the description covers the major categories of checks and even mentions the output format (scored verdict 0-100). It does not list every possible check but provides sufficient context for an agent to understand what the report includes and what to expect as output.

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' is already well-described in the schema ('Company domain to investigate (e.g., stripe.com)'). The tool description does not add additional meaning or constraints beyond what the schema provides, so the baseline score of 3 applies.

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: generating a comprehensive company report with 25+ parallel checks. It distinguishes itself from sibling tools by enumerating the specific data points it covers (WHOIS, SSL, sanctions, DNS, etc.), making its scope explicit and unique.

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: it is the 'full' and 'mega' report option, suggesting it should be used when a comprehensive analysis is needed. However, it does not explicitly contrast with sibling tools like domain_enrich or site_audit, leaving some ambiguity about when to choose this over more specialized 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 carries the burden of conveying behavior. It mentions the cost ($1.00) and the specific categories analyzed, which is useful. However, it does not disclose details like response time, potential limitations (e.g., if domains are unreachable), or whether the analysis is real-time or cached. The cost disclosure adds transparency, 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 and information-dense. It conveys the purpose, cost, inputs, output structure, and evaluation criteria in two sentences. No wasted words, and the most important details (what it does, cost) are 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 moderate complexity (two simple string parameters, no output schema), the description provides a complete picture: what the tool does, what it covers, and what output to expect. The description compensates for the lack of annotations and output schema by detailing the 8 dimensions and the verdict. No significant 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?

The schema coverage is 100%, with parameter descriptions for domain1 and domain2. The description restates the requirement ('Pass two domains') but doesn't add new meaning beyond what the schema provides. It does clarify the output structure (head-to-head comparison), which indirectly aids understanding, but for parameters themselves, it's baseline.

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: a competitive analysis report comparing two domains across 8 specified dimensions. It distinguishes itself from siblings like site_audit or domain_enrich by focusing on head-to-head comparisons and returning a winner per category and overall verdict.

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 when to use it (for competitive analysis between two domains) and lists the exact input requirements. It doesn't explicitly state when not to use it or name alternatives, but the clear focus sets expectations. It could mention that it's not for single-domain audits, but overall adequate.

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 responsibility for disclosing behavior. It indicates real-time data retrieval but does not explicitly state it is read-only, error handling for invalid IDs, rate limits, or any side effects. The mention of 'supports thousands of coins' gives some input expectations, but overall transparency is moderate.

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 and front-loaded with the core purpose. It consists of two short sentences with no unnecessary words or details, effectively conveying the tool's function and input scope.

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 lack of an output schema, the description adequately hints at what the tool returns by listing specific data points (price, volume, market cap, change). It is complete enough for a simple retrieval tool, though it could benefit from specifying response format or default behavior for optional parameters, which the schema already partially covers.

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 parameter semantics are already well-defined in the schema (coin as CoinGecko ID, currency with default 'usd'). The description adds minimal extra meaning, only reaffirming that coin IDs are used. It does not introduce new details about parameter behavior 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 provides real-time cryptocurrency market data including price, volume, market cap, and price change. It specifies the data source (CoinGecko IDs) and scope (thousands of coins), making the purpose unambiguous.

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 does not explicitly mention when to use this tool compared to alternatives. It implies usage for current market data but lacks guidance on scenarios like historical queries or comparisons with sibling tools. Sibling tool names are present in context, but the description itself doesn't reference them.

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 that rates are real-time and that it returns rate, converted amount, and inverse rate. However, it does not mention any limitations, such as rate source, potential delays, or whether it requires network access. For a conversion tool, this is adequate but not rich; it could mention that it does not perform actual transactions or that rates are indicative.

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, front-loaded with the core purpose, and includes essential details (supported currencies, return values) without any fluff. 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 (3 params, no output schema, no annotations), the description is fairly complete. It explains what it does, what it returns, and gives examples of supported currencies. It could mention that the 'amount' is optional and defaults to 1, but the schema already covers that. The description 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?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds context by listing example currencies and stating that it returns rate, converted amount, and inverse rate, which helps understand the output. However, it does not add details about parameter formats (e.g., currency codes are case-sensitive) or the meaning of the 'amount' parameter beyond what the schema says. Baseline 3 is appropriate.

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: 'Currency conversion with real-time rates.' It specifies the resource (currency conversion) and the action (convert), and distinguishes it from siblings by mentioning support for 150+ currencies including fiat and crypto, which is unique among the listed 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 implies usage for converting between currencies but does not explicitly state when to use this tool versus alternatives. It mentions real-time rates and supported currencies, which gives context, but lacks explicit guidance on when not to use it or alternatives. Given the sibling tools include crypto_data and gold_price, which might overlap, more explicit differentiation would be helpful.

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 of disclosure. It explicitly mentions detection of language, script type, Arabic dialect, and highlights special handling of Arabizi and mixed-language text, which goes beyond a generic 'detects language' statement. It implies a read-only operation (detection) without explicit confirmation, but the scope of behavior is well-defined. No contradictions exist.

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, front-loaded with the primary purpose and followed by a concise list of supported languages and special features. Every sentence earns its place—first states the core action, second provides essential scope details. No filler, no redundancy, and highly 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?

There is no output schema, so the description should ideally explain what the tool returns (e.g., a language code, dialect label, confidence score). It lists what it detects (language, script, dialect) but does not specify the response structure or format. Given the tool's complexity (multiple detection dimensions), the lack of return-value info leaves a gap for the agent. However, the description does provide enough to guess basic behavior, but not 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 already provides full coverage for the single parameter 'text' with a description ('Text to analyze (max 10,000 chars)'), so schema coverage is 100%. The tool description does not add any additional meaning about the parameter—it mentions 'any text' but that's redundant with the schema. Since the schema does the heavy lifting, the baseline of 3 is appropriate; the description contributes little beyond existing schema info.

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: 'Detects language, script type, and Arabic dialect from any text.' It specifies the exact resources (language, script, dialect) and lists supported languages and special cases (Arabizi, mixed-language), which distinguishes it from sibling tools like arabic_sentiment or text_summarize. The verb 'detects' is precise and unambiguous.

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 clear context on what the tool does and its supported inputs (languages, scripts, dialects), allowing an agent to infer when to use it. However, it does not explicitly state when not to use it or mention alternative tools, such as arabizi_translate for translation or arabic_sentiment for sentiment, so it lacks explicit exclusions. The context is strong enough for a typical use case but not fully explicit.

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 must disclose behavior. It lists output fields but does not mention potential limitations (e.g., whether the domain must be publicly accessible, rate limits, or whether it performs a live scan or uses cached data). This is a significant gap for a tool that fetches external data.

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 lists the rich set of data returned. It front-loads the purpose and is efficient, though it could be slightly longer to include usage nuance.

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 moderate complexity (multiple data types returned) and no annotations, the description covers output types but misses behavioral aspects like data freshness, dependency on third-party sources, and potential errors. It is adequate for basic understanding but incomplete for nuanced 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 coverage is 100% for the single parameter 'domain', which has a clear description. The description reinforces the parameter by giving examples (e.g., google.com, aramco.com) and connecting it to the enrichment output, but adds no new semantics beyond what the schema already provides.

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 enriches a domain with company info, tech stack, social links, emails, and redirect info. It is specific about the resource (domain) and the type of data returned, but doesn't explicitly distinguish it from siblings like domain_infra or site_audit, which might also relate to 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 implies usage for enriching a domain with business information, but does not explicitly state when to use this tool versus domain_infra or site_audit. It lacks guidance on alternatives or 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 exist, so the description bears full responsibility. It mentions specific actions (DNS brute-force, Certificate Transparency subdomain discovery, IP geolocation) which imply network calls, but it does not disclose potential rate limits, response format, or that it is strictly read-only. It lacks explicit behavioral guarantees beyond listing its scope.

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 core purpose ('Domain infrastructure map') followed by a dense, comma-separated list of features. No wasted words, every element 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's complexity (various DNS records, subdomain discovery, geolocation) and lack of output schema, the description provides a thorough enumeration of what is returned. It does not mention prerequisites or limitations, but it covers the main behavioral aspects adequately for an initial 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?

The only parameter 'domain' is well-described in the schema with an example. The description does not add any additional syntax, constraints, or format details beyond what the schema provides. With 100% schema coverage, the baseline of 3 is appropriate.

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: 'Domain infrastructure map' with a comprehensive list of DNS record types and additional features like hosting provider, CDN detection, subdomain discovery, and IP geolocation. It distinguishes from siblings like 'domain_enrich' by specifying the full scope of DNS records and subdomain discovery techniques.

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 comprehensive domain infrastructure mapping but does not explicitly compare to alternatives (e.g., 'domain_enrich') or state when not to use it. The agent can infer the tool's broad scope, but there is no explicit guidance on when to choose this 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 are provided, so the description must carry the behavioral disclosure burden. It transparently states the cost ($0.50) and that it runs parallel checks, which is valuable. However, it does not clarify behavior when both domain and company are provided, error handling, or data freshness, leaving gaps in transparency for a tool with no annotation safety net.

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 core purpose and packs significant detail (list of outputs, cost justification) without being wordy. Each sentence adds value, though it could be slightly tighter by removing the redundant 'because it runs 5+ parallel checks' explanation.

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 complex tool with no output schema, the description covers the return contents thoroughly, listing all data types and the confidence score range. It also explains the cost rationale. It lacks a mention of output format or duration, but given the exhaustive list, it is largely complete for an AI agent to understand what it will receive.

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 already has 100% coverage with descriptions for both parameters. The description adds that you can pass 'a domain or company name,' implying they are alternatives, which is a slight clarification beyond the schema. But it doesn't elaborate on formats, precedence, or relationship between parameters, so it only meets the baseline for full 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 'Entity due diligence report' with a specific verb 'get' and a comprehensive list of data types (WHOIS, SSL, sanctions, etc.). It distinguishes itself from sibling tools by emphasizing it replaces 10+ separate lookups, making its scope unique compared to more targeted tools like domain_enrich or company_report.

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 phrase 'One call replaces 10+ separate lookups' gives clear usage context: use when you need multiple due diligence data points in one request. However, it does not explicitly mention when not to use it or name alternatives. It implies a comprehensive need but lacks explicit exclusions, so it misses a 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?

With no annotations, the description carries the full burden. It adds meaningful behavioral context by stating 'All DNS-based, zero API keys' (indicating no external dependencies) and specifies the exact checks and score range. It does not disclose potential timeouts or rate limits, but for a read-only audit, this is reasonably 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 two sentences, front-loaded with the core purpose, then enumerates concrete checks and output. Every sentence earns its place with no filler or redundant wording.

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: what it checks, what it returns, and its operational nature. It could have mentioned that the domain must be a public domain or that DNS lookups may be slow, but the information provided is largely sufficient 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 single parameter 'domain' is already well-described in the schema with an example. The description does not add independent parameter-level meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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 an 'Email infrastructure audit' and lists the specific record checks (SPF, DKIM, DMARC, MX, DNSBL, DNSSEC) and output (deliverability score). This distinguishes it from sibling tools like verify_email (which verifies individual addresses) and domain_infra (which may cover broader infrastructure).

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 when to use the tool—when you need to audit a domain's email infrastructure—but provides no explicit guidance on when not to use it or how it differs from alternatives like domain_infra or site_audit. The 'All DNS-based, zero API keys' note offers some context but no comparative 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 provided, the description carries the full burden of behavioral disclosure. It adds context that the data is consumer retail pricing and current, but it does not describe output format, caching, rate limits, or how the defaults of 'all' and 'USD' behave beyond the schema.

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 short sentences that convey all essential scope details without any filler. Every phrase contributes meaning, making it highly concise 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 price lookup tool with two optional, well-described parameters and no output schema, the description covers the likely use case and scope in sufficient detail. A brief mention of the response structure would have made it 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?

Schema coverage is 100% with both parameters already described. The description adds modest context (per gram, retail pricing) but does not materially expand on the parameter semantics beyond what the schema already states.

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 identifies the resource as current retail gold prices per gram, listing specific karats and currencies, which distinguishes it from sibling tools like currency_convert and crypto_data. It lacks an explicit verb like 'get' or 'retrieve,' but the intent is unambiguous.

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 when to use the tool: when current retail gold prices are needed. However, it offers no explicit guidance on when not to use it or which alternative tool might be more appropriate for related financial 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 the burden of behavioral disclosure. It lists outputs and mentions a cross-referenced risk score, but does not explicitly state read-only/non-mutating behavior, potential rate limits, or error conditions. The feature list is informative but lacks depth on side effects or constraints.

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) and front-loaded with the core purpose, then enumerates features efficiently. No filler or redundant phrasing; every sentence contributes to understanding.

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 (1 parameter, no output schema), and the description sufficiently covers what it returns (geolocation, DNSBL, risk score, etc.). It lacks details on error handling or limits, but given the simplicity, it is nearly 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 100% (the 'ip' parameter is fully described as IPv4 with example). The description adds context about the tool's behavior on that IP but does not enhance the parameter's meaning beyond the schema, fitting the baseline of 3.

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 IP address threat intelligence, listing specific data points (geolocation, ASN/ISP, reverse DNS, etc.) and a risk score. It distinguishes itself from sibling tools by focusing solely on IP-related intelligence, unlike domain or email 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 implies usage when IP threat intel is needed but provides no explicit when/when-not scenarios or alternatives. There is no mention of when to choose this over other intelligence tools, so guidance is implied rather than explicit.

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 full responsibility for disclosing behavioral aspects. It mentions the input type and outputs but does not address potential side effects, required permissions, rate limits, or any state changes. The tool appears to be a read-only analysis, but this is not explicitly stated.

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, consisting of two sentences, and uses efficient, direct language. It avoids redundancy and includes all necessary information without 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?

Since there is no output schema, the description lists the types of outputs (plain English explanations, red flags, key obligations, complexity score, risk level) but does not specify their structure, format, or how they are presented. This provides a general idea but lacks detail for a consumer to fully understand the return value.

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 'text' has a description that adds a maximum length constraint (20,000 chars), which is not present in the schema type alone. This provides additional useful context, though it could be more detailed regarding accepted formats or examples.

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 (simplifies legal jargon), specifies input types (contract clauses, terms of service, privacy policy text), and lists the expected outputs (plain English explanations, red flags, key obligations, complexity score, risk level). It is distinct from sibling 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 implicitly indicates the tool is for legal text analysis, but it does not explicitly state when to use it versus alternatives or provide any negative usage conditions. No when-not or alternative guidance is given.

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 behavioral disclosure burden. It mentions supported calculation methods and output contents (sunrise, Hijri date), but does not disclose error handling, rate limits, or behavior for invalid inputs (e.g., unknown city, invalid date). The description gives a reasonable overview but lacks depth.

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, well-structured sentence that immediately conveys the core purpose and key outputs. It is concise, front-loaded, and contains no redundant information, earning a top score.

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 relatively simple with 3 parameters and no output schema. The description covers the main response elements (prayer times, sunrise, Hijri date, calculation methods) and the global scope. It could add details about the response format, but the absence is not critical given the straightforward nature of the 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 input schema has 100% coverage with descriptions for all three parameters, so the description itself adds little semantic value beyond what is already in the schema. It mentions 'any city worldwide' and calculation methods, but these do not clarify parameter usage further. Baseline 3 is appropriate given the schema's completeness.

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: providing Islamic prayer times for any city worldwide, listing the five daily prayers and additional details like sunrise and Hijri date. This distinguishes it from all sibling tools, which cover unrelated domains (e.g., weather, gold price, translation).

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?

While the description implicitly indicates when to use this tool (for prayer times), it does not provide explicit guidance on when not to use it or mention alternative tools. Given that this tool is unique among its siblings, the lack of direct alternatives is acceptable, but a clearer 'use this for prayer times' or note on constraints (e.g., city ambiguity) would improve 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 carries the responsibility of disclosing behavior. It clearly states what the tool returns: toxicity score, flagged words with categories, severity rating, and cleaned text. This gives a good understanding of the output and implies a read-only detection operation, though it doesn't mention potential error cases or rate limits, which are not expected for a detection 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?

The description is concise, consisting of two sentences that efficiently convey the tool's purpose, language support, and output. There is no redundant information or filler; every word contributes to understanding the tool.

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 is no output schema, the description appropriately outlines the return value (toxicity score, flagged words, severity rating, cleaned text) and language support. It provides sufficient context for a user to understand the tool's capabilities and expected results, though it doesn't dive into edge cases or detailed severity scales, which are not critical for typical 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?

The input schema already provides descriptions for both parameters (text and language), covering 100% of them. The description adds some context, such as multilingual support and 'Gulf dialect aware,' but this is already hinted at in the schema's language description. No additional meaning is added beyond what the schema conveys, so the baseline of 3 is appropriate.

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: 'Multilingual profanity and toxicity detection.' It uses a specific verb ('detection') and a clear resource (profanity/toxicity), and it distinguishes itself from sibling tools by its focus on profanity filtering rather than sentiment or translation.

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 clear context that this tool is for profanity and toxicity detection, and it specifies supported languages (English, Arabic with Gulf dialect awareness, Spanish, French). While it doesn't explicitly mention when not to use it or alternatives, the purpose is unambiguous enough that a user would know to select it for profanity filtering tasks.

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 clearly discloses the output behavior: returns agent-ready JSON, needs no further parsing, and extracts a defined set of structured data types. It doesn't mention failure modes or URL constraints, but the non-destructive read-only nature is evident.

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 deliver the purpose, differentiation, output format, and examples of extracted data. There is no redundancy, and the information is front-loaded with the core functionality.

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 sufficiently covers behavior and return format. It could mention JavaScript rendering or failure behavior, but the gap is minor given the simplicity and the availability of sibling tools for broader scraping needs.

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, 'url', has a complete schema description ('Full URL to scrape and structure'), and schema coverage is 100%. The tool description does not add much parameter-specific detail beyond what schema already says, earning the baseline score.

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+resource combination: 'web scraper' that 'extracts and LABELS structured data', with explicit examples like emails, prices, and tables. It clearly distinguishes itself from 'basic scrapers that return raw text', which differentiates it from sibling web_scrape.

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 clear when-to-use guidance: choose this tool when you need labeled structured JSON rather than raw text. It contrasts with 'basic scrapers' as an implicit alternative, though it does not explicitly name sibling web_scrape or state 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?

No annotations are provided, so the description carries the full burden. It does disclose the return type (base64) and all included metrics, which is beneficial. However, fundamental behavioral details—such as rate limits, authentication needs, URL accessibility expectations, and the exact difference in behavior when format='image'—remain undisclosed.

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 that front-load the action and follow with a concise list of return values. No wasted words; it is appropriately minimal 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?

For a tool with only two parameters and a read-only purpose, the description covers the essential context: what it does, what it returns, and the format. It lacks minor details like timeout behavior or max image size, but given the low complexity, these are not critical gaps. The absence of an output schema is mitigated by the explicit enumeration of return fields.

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 100%, with both parameters (url and format) already documented. The description adds no additional parameter semantics beyond the schema; it never explains the allowed values for format (beyond the schema's own text) or the default behavior. Baseline of 3 applies because the schema is self-sufficient.

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 'Captures a website screenshot as PNG/JPEG with performance metrics' and enumerates the exact return data (base64 image, Lighthouse score, FCP, LCP, TBT). This specific verb+resource pairing leaves no ambiguity about what the tool does, even without comparing to 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 (any task needing a webpage screenshot with performance metrics) but does not explicitly say when to use this tool over alternatives like web_scrape or site_audit, nor does it mention when not to use it. The guidance is implicit but not direct.

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 that it runs parallel checks, returns a scored report with actionable findings, and mentions the cost ($0.50), which is useful behavioral context. Could add more about failure modes or timeouts, but it's a solid disclosure.

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 paragraph, but it's information-dense and well-structured, listing checks clearly. It's slightly long but every sentence adds value. Adding line breaks could improve readability, but it's still concise.

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 moderate complexity (multiple checks) and the simple, fully covered schema, the description provides complete context: what it does, what it returns, and even pricing. No output schema exists, but the description covers the return format. It's comprehensive enough.

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 already describes the URL parameter with an example, so coverage is 100%. The description doesn't add much parameter semantics beyond that, but it's redundant given the schema. Baseline 3 is appropriate.

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 ('Runs') and a clear resource ('Website security and SEO audit'), and explicitly lists the five distinct checks performed. This clearly distinguishes it from sibling tools like 'email_audit' and 'web_scrape'.

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 when to use it: for comprehensive security and SEO audits. It doesn't explicitly state when not to use it or compare with alternatives, but the detailed scope makes the use case evident. Slight deduction for lacking explicit 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 the full burden. It discloses that the tool uses extractive summarization (no paraphrasing), returns sentences in original order, and provides top keywords, which are helpful behavioral traits. However, it does not mention potential limitations like sensitivity to punctuation, handling of very short texts, or whether the output is deterministic, which could be important for user expectations.

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, one sentence, and front-loads the key action ('Extractive text summarization') followed by method and output. Every word earns its place without 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?

Given the tool's moderate complexity (2 params, no nested objects, no output schema), the description covers purpose, method, output, and keyword feature. It lacks details on edge cases or output format, but the absence of an output schema means a brief description might suffice. It is complete for basic 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 coverage is 100%, and the description adds context by explaining that it scores sentences by keyword frequency and returns top sentences, which implies how max_sentences is used. The description does not add new details about parameters beyond what the schema already states (e.g., default for max_sentences is in schema). Since schema is thorough, baseline 3 is appropriate.

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 performs extractive text summarization, specifies the method (keyword frequency), and notes it returns important sentences in original order plus top 10 keywords. This distinguishes it from siblings like 'legal_simplifier' and 'detect_language' by focusing on extractive summarization rather than translation or simplification.

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 summarization tasks but does not explicitly state when to use this versus alternatives like legal_simplifier or detect_language. It does not provide exclusions or prerequisites, such as requiring English text or being suitable for short texts. However, the clear purpose offers some guidance on appropriate 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 full responsibility for disclosing behavior. It effectively communicates that the tool performs multiple actions (checks platforms, categorizes, enriches GitHub, checks domains) and implies a non-destructive, read-only report generation. It does not mention potential limitations like rate limits or failure modes, but given the tool's nature, the key behaviors are transparently outlined.

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 short sentences that front-load the tool's identity ('Username OSINT report') and immediately enumerate specific capabilities. Every word adds value, with no redundancy or filler. It efficiently conveys the full scope of the tool without unnecessary elaboration.

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 complexity (scanning 25+ platforms, enrichment, domain check), the description provides a solid overview of what to expect. Since there is no output schema, it appropriately indicates that a 'report' is generated, though it doesn't specify the report structure or content. It also doesn't mention any prerequisites or edge cases, but for a single-parameter tool, the description is reasonably complete and sets correct expectations about the tool's multi-step behavior.

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 fully documents the sole parameter 'username' with a simple description ('Username to investigate'). Since schema description coverage is 100%, the description adds no additional semantic value beyond what the schema already provides. The baseline score of 3 is appropriate because the schema handles parameter documentation completely.

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: it performs OSINT on a username by checking 25+ specific platforms, categorizing results, enriching GitHub profiles, and checking domain availability. This is a specific verb ('checks') with a clear resource (username) and distinct capabilities that differentiate it from sibling tools like domain_enrich or web_scrape, which focus on different objectives.

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 context (investigating a username across multiple platforms) but does not explicitly state when to choose this tool over alternatives or provide exclusions. It lacks direct guidance such as 'use when you need a comprehensive username footprint' or 'instead of web_scrape for username-specific data.' The implied usage is clear enough, but no explicit comparisons are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does a solid job by enumerating the verification steps and stating that a risk score is returned. It does not mention potential network dependencies, failure modes, or privacy implications, but the core behavior is clearly conveyed.

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, well-structured sentence that front-loads the purpose and then lists specific checks. It is concise, information-dense, and contains no filler or redundant content.

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 the essential behavior and the return value (risk score). It could be more complete by specifying the risk score range or output format, but overall it gives an agent enough context 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?

The input schema already fully describes the single parameter 'email' as 'Email address to verify' (100% coverage). The description adds context about what verification entails but does not add new parameter-level semantics beyond the schema, so the baseline score of 3 is appropriate.

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 identifies the tool's function: verifying an email address through format validation, domain/MX checks, disposable/free/role-based detection, and returning a risk score. It is specific and action-oriented, though it does not explicitly differentiate itself from sibling tools like email_audit.

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 intended use is implied by the description: use this tool when you need to verify an email address and assess its risk. However, there is no explicit guidance on when to choose verify_email over alternatives such as email_audit, nor any exclusions or prerequisites.

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 states the tool 'Returns' data, which implies a non‑mutating read operation, and it does not claim any side effects. However, it does not explicitly state that it is read-only, nor does it disclose potential limitations (e.g., city not found, international coverage caveats). Thus, it gives some transparency but lacks explicit disclosure.

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 that provide the core purpose and the exact return fields. No unnecessary or redundant words. Front-loaded purpose and then scoped output. Every sentence contributes to understanding.

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 read-only weather tool with one parameter and no output schema, the description lists the expected return fields (temperature, feels-like, humidity, precipitation, wind speed/direction, weather condition). It also explicitly states that it covers any city, which addresses the geographic scope. Although it does not describe the concrete structure of the response (as no output schema exists), the described fields are sufficient for a user to know what data 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 schema has 100% description coverage with a clear city description including examples. The description adds no new semantic meaning beyond the schema's own parameter explanation. It does mention 'any city worldwide', but that is already a reasonable inference from the schema's 'City name' and examples. Since the description does not add extra context, it stays at the baseline per rubric for full 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 the tool provides 'Current weather conditions for any city worldwide' and lists the specific data fields to retrieve directly, using a specific verb 'Returns'. It naturally distinguishes from sibling data tools like currency_convert or gold_price, and there is no other weather tool in its sibship.

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 getting current weather conditions, which is clear from the title and content. It does not explicitly mention alternatives, but since no sibling tool overlaps with this function, the context is unambiguous. No explicit exclusions are provided, but the purpose is self-evident.

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 output format (cleaned text, page title, meta description, up to 50 links) and notes that scripts/styles are removed. However, it does not mention potential side effects such as redirects, error handling, rate limits, or any other behavioral aspects that might affect the agent's decision, especially given the lack of 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 and free of redundancy. Every sentence adds value—stating the action and the specific output format—without unnecessary elaboration.

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 is no output schema, the description adequately explains what the tool returns (text, title, meta description, links). It covers the core expected behavior but omits details about error scenarios, URL validation, or response formatting, which could be considered minor gaps but not detrimental.

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?

There is only one parameter (url) and its schema description fully covers it (100% coverage). The tool description adds no additional detail beyond the schema, so per the rubric, a baseline of 3 is appropriate.

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 extracts text content, links, title, and metadata from a URL. It is specific about the verb and resource, but does not explicitly differentiate from sibling tools like screenshot or scrape_structured, though the name and description strongly imply raw text extraction.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or typical use cases, leaving the agent to infer appropriateness from the name and description alone.

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