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 ($0.01 USDC on Base) but fails to mention other behavioral traits such as read-only status, rate limits, or potential idempotency. For a tool with zero annotation coverage, this is a significant gap.

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 cost without extraneous information. Every phrase 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?

For a simple tool with no parameters and no output schema, the description covers the core purpose and a key behavioral note (cost). It is sufficiently complete for an agent to understand the tool's function, though it could briefly hint at 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?

Input schema has zero parameters, so the baseline is 4 per guidelines. The description adds no parameter information (unnecessary) but provides the cost context, which adds 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 specifies a clear verb ('overview') and resource ('global crypto market'), and lists specific data points (total market cap, 24h change, BTC/ETH dominance). It distinguishes from sibling tools like crypto_movers, crypto_price, and crypto_trending by focusing on a broad market snapshot.

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 a high-level market snapshot but does not explicitly state when to use this tool versus alternatives or when not to use it. The cost note provides some practical guidance, but no exclusions or context for sibling 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?

No annotations are provided, so the description carries full burden. It discloses a cost of $0.01 USDC on Base, which is a behavioral trait. However, it does not mention whether the tool is read-only, any rate limits, or what happens on error.

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 sentence with an additional tagline about cost. It is concise and front-loaded with the core purpose, but could be slightly more structured.

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 specify the format of the returned data (e.g., array of objects with fields like name, symbol, change percentage). It only states costs and target users. For a simple list tool, it is adequate but leaves some context missing.

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, and the schema coverage is 100%. With no parameters, the description cannot add parameter meaning beyond the schema. Baseline is 4.

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 'Top gainers & losers (24h) across the top 100 coins', which clearly identifies the tool's purpose as listing the best and worst performing coins over a 24-hour period. It distinguishes from siblings like crypto_price (single price) and crypto_trending (trending coins).

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 mentions 'for trading agents', implying use in trading contexts, but does not explicitly specify when to use this tool versus alternatives like crypto_market or crypto_price. No when-not-to-use or exclusions are provided.

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 ($0.01 USDC on Base), which is important for an agent to know. No annotations exist, so description carries full burden. Lacks details on rate limits, payment failure handling, 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?

One efficient sentence plus a cost note. Every part 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?

Covers purpose and cost, but missing output format, coin availability, and confirmation of real-time nature. Adequate for a simple one-param 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 one parameter (symbol) with schema description 'Coin symbol, e.g. eth' covering 100% schema. Description adds no extra 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 it provides live crypto price, 24h change, market cap, and volume for any coin. Distinguishes from siblings like crypto_market, crypto_movers, and crypto_trending by specifying the exact data points.

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?

Mentions 'for trading agents' as a use case, implying the tool is intended for trading scenarios. However, it does not provide explicit guidance on when not to use it or alternatives among 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?

The description discloses the cost ($0.01 USDC on Base), which is a behavioral trait not present in annotations. However, it omits other behavioral details such as rate limits, data freshness, 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?

The description is extremely concise: two sentences front-loading the core purpose and cost. 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?

Given the tool's simplicity (no parameters, no output schema), the description is largely complete. It covers what the tool does and the cost. Could be enhanced by mentioning the output type (e.g., list of coin names) but is 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?

The input schema has no parameters, so the description does not need to add parameter explanations. With 100% schema coverage and zero parameters, a baseline score of 4 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 returns 'The coins trending right now,' specifying its purpose for trading and research agents. It implicitly distinguishes from siblings like crypto_price by focusing on trending data rather than specific prices, but lacks explicit differentiation.

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 no guidance on when to use this tool versus alternatives like crypto_market or crypto_movers. It states it is for trading and research agents but does not specify conditions 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?

No annotations are provided, so the description carries full burden. It discloses a monetary cost ($0.01 USDC on Base), which is a key behavioral trait. However, it does not mention rate limits, authentication requirements, or output format, leaving gaps in 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?

Two sentences concisely convey purpose and cost. Every word is necessary, and the description is front-loaded with the action and outputs, then the cost. No unnecessary 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?

The tool is simple (one param, no output schema), and the description covers core purpose and cost. However, it omits details like whether extraction handles dynamic content, required permissions, or return structure. Given the low complexity, it is mostly complete but has minor 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 sole parameter (url) is described in the schema as 'Website URL.' The description adds no additional semantics beyond the schema, and with 100% schema coverage, the baseline is 3. The description does not enhance understanding of the parameter beyond its basic 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 extracts emails, phone numbers, and social links from any website for lead enrichment. It uses a specific verb (extract) and resource (contacts), and the purpose is well-defined, distinguishing it from sibling tools like extract_text and page_metadata.

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 use for lead enrichment and prospecting but provides no explicit when-to-use or when-not-to-use guidance. It does not differentiate from similar siblings like extract_text or site_audit, leaving the agent to infer 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 must carry the burden. It discloses cost and that the extracted text is clean of nav/ads, but does not mention rate limits, authentication needs, error handling, or handling of JavaScript-rendered pages.

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 use case, and each sentence adds distinct value 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 tool has only one parameter and no output schema, the description lacks information on return format, error handling, or constraints (e.g., URL length). It covers the essential purpose but omits operational details.

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% coverage for the single 'url' parameter, describing it as 'Page URL'. The description adds no further semantic information beyond that.

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' and the resource 'clean main text content from any web page', and distinguishes from siblings like 'page_metadata' and 'extract_contacts' by specifying the focus on main text for research and summarizing 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?

The description mentions the tool is 'for research & summarizing agents' but does not provide explicit guidance on when not to use it or how it compares to siblings like 'extract_contacts' or 'page_metadata'.

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 data source (ECB) and cost ($0.01 USDC on Base), which is helpful. However, it does not mention rate limits, data freshness, error behavior, or whether it is read-only. Since it's a conversion tool, assumed read-only, but more 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 very concise: one sentence that captures the purpose, data source, and cost. Every part earns its place. It is front-loaded with the 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?

No output schema is provided, so the description should clarify what the tool returns (e.g., rate, converted amount). It only says 'Currency conversion + live exchange rate' which is vague. With 3 parameters and no output schema, more detail on the response 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?

Schema description coverage is 100%, with each parameter described. The description adds no extra meaning beyond what is already in the schema (e.g., 'From currency' is already documented). It mentions amount default 1, but that's in schema too. 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 it does currency conversion and live exchange rates using ECB data, and the title explicitly says 'Currency Conversion' with a cost note. This distinguishes it from sibling tools like crypto_price or crypto_market, which focus on cryptocurrencies.

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 fiat currency conversion but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not or exclusion conditions. The sibling set helps differentiate, but the description lacks direct 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, the description carries full burden. It discloses return outcomes (approved/pending_human/denied), cost ($3.00 USDC on Base), and human-in-the-loop accountability. Lacks details on side effects or prerequisites, but sufficient for a non-destructive approval 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?

Four sentences, each earning its place: purpose, usage context, return values, and unique value proposition (human-in-the-loop + cost). Front-loaded with the core function. 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?

Despite no output schema, description explains return outcomes and cost. Does not cover error cases or prerequisites (e.g., having a USDC balance), but adequately sets expectations for an approval 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 description coverage is 100% with clear parameter descriptions. The tool description adds no extra meaning beyond the schema, so 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 'HUMAN-VERIFIED approval for an agent action' with a specific verb and resource. It distinguishes from sibling tools (all data lookups or extraction) by being the only approval/action 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?

Explicitly says 'Before an agent pays, signs, or acts for its human' which clarifies when to use. Does not explicitly state when not to use, but context from siblings implies it's for actions needing human sign-off, not data 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It reveals the cost ($0.10 USDC) and the scope of returned information (multiple due diligence fields). However, it does not disclose any potential side effects, authorization requirements, or rate limits, leaving minor 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 two sentences: the first lists content, the second gives use case and cost. It is maximally concise with no redundant words, front-loading the key purpose and differentiation.

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 complex with no output schema. The description lists high-level categories of returned data but lacks specifics on structure, formatting, or edge cases. For a paid due-diligence tool, more detail on output format 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?

Schema coverage for the single 'target' parameter is 100%. The description adds value by stating it works for 'any company or website' and listing example outputs, but does not provide additional constraints or format details beyond the schema's 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 explicitly states the tool produces a 'full due-diligence dossier' on any company or website, listing specific data points (legitimacy, domain age, contacts, social presence, scam red flags). This clearly defines its purpose and differentiates it from sibling tools like merchant_trust or extract_contacts, which cover only subsets.

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 this tool is for 'counterparty research & background checks' and positions it as consolidating multiple checks into one call ('one call instead of ten'). While it doesn't explicitly list exclusions or when not to use, the context strongly implies its primary use case, providing adequate 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. Discloses cost ($0.05 USDC), output types (verdict + signals), and implied non-destructive nature (read-only lookup). Does not detail rate limits or authentication, but adequate for a 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?

One concise sentence covering purpose, output elements, use case, and cost. Front-loaded with the key question. 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 single parameter, no output schema, and simple tool, description fully explains outputs (verdict, signals) and usage context (before payment, cost). No 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?

Single parameter 'url' with schema description 'Store URL'. The tool description adds no further detail beyond the schema. Since schema coverage is 100%, 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 asks 'Is this online store safe to buy from?' and specifies outputs: scam/fraud check, trust verdict (safe/caution/avoid), and store signals. It distinguishes from sibling tools like trust_check and crypto_market by targeting shopping/commerce agents before payment.

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?

States use case: 'counterparty trust for shopping & commerce agents before they pay'. Implicitly suggests when to use (before payment) but lacks explicit when-not-to-use or comparison with alternatives. Cost disclosure ($0.05) adds 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 the full burden. It adds the cost of $0.01 USDC, which is useful. However, it does not disclose other behavioral traits such as read-only nature, rate limits, or authentication requirements, leaving gaps for an AI 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 two sentences, front-loaded with the extracted fields, and includes cost information. Every word contributes value with no repetition or unnecessary 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?

Given the tool's simplicity, the description sufficiently explains what metadata is extracted and the cost. There is no output schema, so the description serves as the primary source of return value information. It covers all essential aspects for a lightweight extraction 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 100% with a single 'url' parameter described as 'Page URL'. The description adds 'any URL' but does not provide additional semantic details beyond what the schema states. 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 extracts specific metadata (title, description, OpenGraph image, favicon, canonical) from any URL, with a clear verb 'extract' and resource 'URL'. It distinguishes from sibling tools like extract_text and site_audit by focusing on metadata 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?

The description does not provide explicit guidance on when to use this tool versus alternatives like extract_text or site_audit. It implies usage for link previews and summaries but lacks exclusionary criteria or context for selection.

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, scoring, and output (verdict, fixes) but lacks details on error handling or behavior with invalid URLs.

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 convey purpose, output, and cost efficiently. 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 single parameter, no output schema, and no annotations, the description covers core functionality, scoring, and cost. Minor gap: output format details.

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 'url' with schema description. Tool description adds context ('Is this website legit?') but doesn't enhance parameter understanding beyond schema. Baseline 3 for 100% 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 audits a website for trustworthiness, SEO, and contactability, providing a score and verdict. It distinguishes itself from sibling tools like 'trust_check' by specifying the full audit scope.

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 'Due diligence in one call' implies when to use it. While it doesn't explicitly contrast with siblings, the context makes it clear this is for comprehensive website evaluation.

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 output includes price, market data, and a trust assessment, and mentions cost. However, it does not disclose potential side effects, error behavior, or data freshness beyond the trust vetting.

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 unique value proposition. Every sentence adds information without 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?

For a tool with one parameter and a clear output scope (price + market + trust), the description covers the main features and cost. It lacks details on error states or prerequisites, but given the simplicity, it is 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?

With 100% schema coverage, the description adds no additional parameter detail beyond the schema's 'Coin symbol, e.g. btc'. The baseline of 3 is appropriate as the description does not enhance semantic 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 the tool provides token intelligence combining market data with a scam/rug-risk assessment. It uniquely positions itself among siblings like crypto_price and trust_check by explicitly noting 'Only DYOE fuses market data with trust.'

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 the tool is for when both market data and trust assessment are needed, and it mentions the cost. However, it does not explicitly state when not to use it or compare to alternatives like crypto_price or trust_check individually.

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 cost ($0.01 USDC on Base) and the return payload (verdict, trust_score, domain age, red_flags). It does not mention failure modes or side effects, but it is a read-only check with no destructive behaviour.

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 concise sentences. The first sentence states the purpose and checks, the second specifies outputs and cost. No unnecessary words or 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?

Given the tool's simplicity (one parameter, schema covers it, no output schema), the description adequately explains what it does and returns. It could mention edge cases or errors, but is complete enough 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?

The schema has 100% coverage with a single parameter 'target' described as 'Domain/URL/wallet to verify, e.g. example.com'. The tool description adds no additional meaning beyond the schema, so a 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's purpose: checking if a counterparty is safe before trust or payment, with specific checks (scam, fraud, phishing, rug-risk) and outputs (verdict, trust_score, domain age, red_flags). It distinguishes itself from sibling tools like merchant_trust and token_intel by focusing on general trust checks rather than specific 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 provides clear usage context: 'before an agent trusts or pays any website, business, wallet, or endpoint.' It implies when to use but does not specify when not to use or mention alternatives among the 15 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?

With no annotations, the description bears full burden for behavioral disclosure. It mentions a cost ($0.01 USDC) and implicitly indicates a read operation (conditions and forecast), but does not discuss rate limits, authentication, or any side effects. This is adequate for a simple read tool 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 two brief sentences, front-loaded with the core capability and followed by cost information. Every word is functional; no redundancy or filler. Perfect conciseness for a simple 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?

For a straightforward tool with one parameter and no output schema, the description is nearly complete. It explains what data is returned (current + 3-day forecast) and the cost. It does not specify the output format or provide an example, but this is not critical given the simplicity. It is sufficient for an agent to understand 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 already covers the single 'city' parameter with an example, achieving 100% coverage. The description adds the context 'any city' and 'for travel & planning', but does not provide additional format or validation rules beyond the schema. Baseline 3 is appropriate as schema does the heavy lifting.

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 conditions + 3-day forecast for any city' for travel and planning agents. The verb is implicit but unmistakable, and the resource (weather for a city) is explicit. It is easily distinguished from all sibling tools, which cover unrelated domains like crypto, extractors, and identity checks.

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 travel and planning agents, but provides no explicit when-to-use or when-not-to-use guidance. There are no sibling weather tools to cause confusion, so the minimal guidance is acceptable but not proactive.

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 ($0.01 USDC on Base) but does not disclose response format, length of summary, disambiguation handling, or any rate limits. It lacks detail about behavioral traits beyond being a factual summary.

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 with clear front-loading of purpose and inclusion of cost. No wasted words, but the second sentence could be integrated gracefully. Still 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 is adequate: it covers source, purpose, and cost. However, it omits key context like summary length, formatting, and how to handle ambiguous topics or missing pages.

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 an example ('e.g. Bitcoin'). The description adds 'any topic' and 'from Wikipedia,' but these are implicit from the tool name and schema. Baseline is 3; the description provides minimal additional meaning beyond what the schema already conveys.

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 'Factual summary of any topic from Wikipedia — for research agents,' clearly indicating the tool summarizes Wikipedia topics for research purposes. It distinguishes from sibling tools, which are market data, contact extraction, text extraction, or weather, none of which provide Wikipedia summaries.

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 research agents,' implying use for factual research, but does not specify when to avoid this tool or provide alternatives. It does not mention when not to use it, such as topics not on Wikipedia or needing real-time data.

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