GoCreative Agent API

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

No annotations are provided, but the description discloses cost ($0.005–$0.05 per call) which adds transparency. It does not mention other behavioral traits like read-only nature, rate limits, or data retention, but the name implies non-destructive analysis.

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 long, no wasted words. It front-loads the main purpose, includes an example, and provides cost information 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?

The tool has one parameter, no output schema, and no annotations. While it explains the input and cost, it does not describe the return format or structure of the audit results, which is important for an agent to process the 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?

Only one parameter with 0% schema coverage. The description adds meaning by specifying the 'owner/repo' format (e.g., 'vercel/next.js'), which is not evident from the schema's title 'Owner Repo'.

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 audits a GitHub repo for security signals like CVEs, last commit age, license, and contributor count. It uses a specific verb and resource, and the purpose is distinct from sibling tools like lookup_github or enrich_github.

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 to use for OSS supply-chain risk scoring and provides an example call. However, it does not explicitly mention when not to use or compare to alternatives like lookup_github_releases.

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

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

No annotations provided; description adds cost disclosure but lacks details on side effects, authentication, rate limits, or error handling. The read-only nature is implied but 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?

Three sentences: purpose, usage context, example with cost. No redundant text; every sentence adds value. Front-loaded with the verb and resource.

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

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

Covers the metadata type (description, pricing, last build, recent runs) and cost, which is sufficient given the tool's simplicity and lack of output schema. No apparent 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?

Input schema has 0% description coverage, but the description compensates with an example ('apify~instagram-scraper') and clarifies the parameter is for a public Apify actor ID, adding necessary meaning.

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 the specific verb 'Get metadata' and resource 'public Apify actor', listing exactly what metadata is retrieved. Distinguishes from sibling enrichment tools by specifying the Apify platform.

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 tells when to use: 'when researching Apify scrapers or comparing actor coverage'. This contrasts with sibling tools like scrape_* which extract data from sites, providing clear 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?

Since no annotations are provided, the description carries the full burden. It discloses the headless browser behavior, full JS render, return of HTML and screenshot URL, and mentions cost. It does not detail error handling or limits, but adequately covers the core behavior.

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

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

The description is extremely concise: two sentences, an example, and a cost note. Every sentence adds value, and the key information is front-loaded.

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

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

The tool has no output schema, so the description must cover return values. It states 'returning final HTML and screenshot URL', which is sufficient. It also includes cost and an example. Could mention error scenarios, but completeness is good for a simple fetch 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 schema has only one parameter 'url' with no description. The description adds the example and implies it is a URL string, but does not elaborate on format or constraints. With 0% schema coverage, it provides minimal 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 that the tool performs a headless-browser fetch of a URL with full JS render, returning final HTML and screenshot URL. The verb 'enrich' and resource 'browserbase' are specific, and the description distinguishes it from plain fetch alternatives by mentioning JS rendering.

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

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

Explicitly says 'Use when the target page is SPA/JS-rendered and a plain fetch returns empty HTML', providing clear when-to-use guidance. It does not explicitly state when not to use, but the context is well-defined.

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 notes 'Headless-browser' and 'returning a CDN screenshot URL,' which indicates the tool runs a browser in the background and provides a hosted image URL. It also includes cost information ($0.005–$0.05 USDC per call) and an example. No annotations are provided, so the description carries the full burden and does so adequately.

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

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

The description is extremely concise: two sentences plus an example and cost note. It is front-loaded with the core purpose and adds no unnecessary words or fluff.

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

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

Given the tool has only one required parameter and no output schema, the description fully covers what the agent needs: input (URL), output (CDN screenshot URL), use case, cost, and an example. Nothing essential is 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 schema has 0% description coverage, so the description must compensate. It provides an example call with a URL and implies the parameter is a web address, but it does not add detail beyond what the schema already provides (type string, title 'Url'). The example helps clarify usage but is not extensive.

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 'screenshot of a URL, returning a CDN screenshot URL,' which is a specific verb and resource. It distinguishes itself from sibling enrich tools that target specific platforms (e.g., enrich_instagram) and from lookup/scrape tools by emphasizing visual snapshots.

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

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

The description explicitly says 'Use when you need a visual snapshot for a report, slide, or QA pipeline,' providing clear context for when to use the tool. However, it does not mention when not to use it or suggest alternatives, which would be helpful given the many sibling tools.

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

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

No annotations provided, so description must disclose behaviors. It mentions cost and lists return metadata, but does not disclose read/write nature, rate limits, or data freshness. It is adequate but not fully 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?

Description is brief (three sentences including example and cost). Each sentence adds value. Example call is provided. Could be slightly more structured, but no waste.

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

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

Given the simplicity (1 param, no output schema, no annotations), the description covers purpose, usage, cost, and partial behavior. Lacks explicit return structure but lists fields. Sufficient for a narrow enrichment tool.

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

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

Schema coverage is 0% (no description in schema). Description only repeats 'given just a domain' and provides an example, but does not clarify format (e.g., protocol, TLD requirements) or any constraints. Minimal added value for the single parameter.

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?

Clear verb 'enrich' with specific resource 'company' and explicit output metadata fields (industry, employee size, etc.). Distinguishes from siblings by specifying input is a domain.

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

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

States when to use: 'Use whenever you need company context for a B2B lead, prospect, or competitor.' Does not provide explicit when-not or alternatives, but context combined with sibling names (many enrich_* tools) makes selection clear.

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

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

No annotations are provided, so the description carries the full burden. It does not disclose whether the operation is read-only, requires authentication, has rate limits, or what happens on errors. The cost range is mentioned but insufficient for full transparency.

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

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

The description is three sentences, each serving a clear purpose: what the tool does, when to use it, and an example with cost. No redundant information, and the most important information is front-loaded.

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

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

Given no output schema or annotations, the description covers the basic input (username) and output (enriched fields) but omits output format, error handling, and data freshness. It is adequate for a simple tool but has gaps.

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

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

The schema has a single 'username' parameter with no description (0% coverage). The description provides an example ('torvalds') but does not add constraints like case sensitivity or validity rules, so it adds minimal 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?

The description explicitly states the tool 'Enrich a GitHub user profile' and lists specific fields (public repo count, followers, hireable flag, top languages, join date). This clearly distinguishes it from sibling tools like lookup_github_user (which likely returns basic profile info) and other enrich_* tools for different platforms.

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

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

The description says 'Use for developer-lead qualification or recruiter sourcing,' providing clear context. However, it does not explicitly contrast with alternatives like lookup_github_user or mention when not to use it, so it lacks exclusions.

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

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

No annotations are provided, so the description carries full responsibility. It mentions the cost per call, which is useful, but it does not disclose other behavioral traits like rate limits, data freshness, authentication requirements, or any side effects. The description is minimal in this regard.

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

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

The description is concise with four sentences covering purpose, usage, an example, and cost. Every sentence adds value, and there is no redundant or unnecessary text. It is well-structured and front-loaded.

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

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

For a simple tool with one parameter and no output schema, the description covers the basics: purpose, usage, example, and cost. However, it lacks details about the output structure (e.g., format of rating distribution, length of recent text) and potential limitations.

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

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

The only parameter, place_id, is explained in the description as a Google place_id, with a concrete example. Since schema description coverage is 0%, the description adds meaning, but it does not explain what a place_id is or how to obtain one, leaving some ambiguity.

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

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

The description clearly states the tool aggregates Google Maps reviews by place_id, producing rating distribution and recent review text. While it identifies the specific resource and action, it could be more explicit about the exact output structure to fully distinguish from other review-related tools.

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

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

The description provides a clear use case: when you have a place_id and need structured review data. However, it does not mention when not to use this tool or suggest alternatives, such as if a place_id is unavailable or if a different review source is needed.

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

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

No annotations are provided, so the description carries the full burden. It mentions the cost ($0.05 USDC) and the username format (without @), but does not disclose error handling, rate limits, authentication requirements, or what happens if the profile is not found. It implies a read operation but lacks detail.

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 (three sentences), front-loaded with purpose, and includes parameter details and cost. However, the structure could be improved by separating parameter notes from behavioral notes.

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

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

Given the single parameter and lack of output schema, the description covers the core functionality and cost but omits details on return format structure, pagination for followers, and error states. It is adequate for basic use but not deeply informative.

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 a single required parameter 'username' with no description. The tool description adds critical semantics: 'Instagram handle without the @ sign.' This goes beyond the schema and aids correct usage. Also mentions the cost parameter, which is indirectly related.

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

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

The description clearly states 'Get an Instagram profile's followers, bio, business category, verified status, etc.', with a specific verb and resource, and the list of attributes distinguishes it from sibling tools that target other platforms (enrich_linkedin, enrich_tiktok, enrich_x) or different actions (search_instagram_hashtag).

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?

Usage is implied by the tool name and description (for Instagram profile enrichment), but there is no explicit guidance on when to use versus alternatives, no 'when not to use' conditions, and no mention of prerequisites or limitations.

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

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

Discloses cost and gated nature of data, adding behavioral context beyond annotations (none provided). Lacks details on error handling, rate limits, or authentication requirements.

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

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

Three sentences, each serving a purpose: function description, parameter formatting, and cost transparency. No redundancy, well front-loaded.

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

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

Given no output schema, description adequately outlines returned data (headline, company, experience snapshot). Could improve by noting error states or additional constraints.

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?

Provides crucial meaning for the 'username' parameter that is missing from the input schema (0% coverage): specifies format as LinkedIn vanity URL slug with a concrete example.

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

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

Clearly states the tool gets a LinkedIn profile and lists specific data fields (headline, company, experience snapshot). Differentiates from sibling tools which operate on other platforms.

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

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

Implies use when LinkedIn profile data is needed; mentions cost as a practical consideration. Does not explicitly state when not to use but sibling differentiation helps.

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 implies a read operation ('aggregate') and discloses cost, but does not explain any side effects, privacy implications, or limits. The description could be more explicit about the tool being non-destructive.

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 at three sentences, front-loads the purpose, and includes an example and cost without redundancy. Every sentence adds value.

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

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

Given the tool's simplicity (one parameter, no nested objects, no output schema), the description covers the return values (rating, review count, review snippets) and use cases. It could mention pagination or response format, but it is sufficiently complete for an agent to understand 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 only one parameter 'query' with type string and no further description. The description adds an example ('blue bottle coffee oakland'), which clarifies the expected format and meaning. With 0% schema coverage, this compensation elevates the 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 clearly states it aggregates Google Maps reviews for a local business, listing specific outputs (rating, review count, recent review snippets). This is a specific verb+resource and distinguishes from sibling enrich_* tools by mentioning Google Maps.

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 use cases: local-SEO research, competitor monitoring, or restaurant/service intelligence. It gives context for when to use, though it does not explicitly state when not to use or mention alternatives. Still, the guidance is clear and 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?

No annotations exist. Discloses cost ($0.005–$0.05 per call), which is helpful, but omits behavioral details like rate limits, authentication requirements, or error behavior.

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

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

Three sentences: purpose, use case, cost. No unnecessary words, front-loaded with key information.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, use context, and cost. Missing details on return structure or error handling, but acceptable for its complexity.

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

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

Only one parameter (artist_id) with 0% schema description coverage. The description provides an example call but does not explain the expected format or constraints of the artist ID.

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

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

Clearly states the verb 'Enrich', resource 'Spotify artist profile', and specific data fields (monthly listeners, follower count, top tracks, genres). Distinguishes from siblings targeting other platforms.

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

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

Provides explicit use cases ('music marketing, A&R research, or playlist-pitching workflows') but does not mention when not to use or alternative tools.

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

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

No annotations are provided, so the description must cover behavioral traits. It includes cost information ($0.005–$0.05 per call) and an example call, but does not disclose rate limits, authentication requirements, data freshness, or error handling. This is adequate for a simple enrichment tool but incomplete.

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

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

The description is very concise: two sentences plus an example and cost. It front-loads the purpose, then provides a concrete example. No wasted words; every sentence earns its place.

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

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

Given the tool's simplicity (1 param, no output schema, no annotations), the description covers purpose, usage context, example, and cost. It lacks return format details, but the output is likely self-explanatory (the enriched data). Minor gap: no mention of what happens on error (e.g., username not found). Still, it is mostly complete for an enrichment tool.

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

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

The input schema (100% coverage) already defines the 'username' parameter. The description adds an example ('zuck') implying it expects a Threads handle. This adds marginal value beyond the schema. With 0% schema description coverage, the description provides minimal additional semantics.

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

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

The description clearly states the tool's action ('Enrich'), resource ('Threads (Meta) profile'), and the specific data added (follower count, bio, verified status). It also provides a use case ('cross-platform social-presence checks'), which differentiates it from sibling enrichment tools targeting other platforms.

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

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

The description explicitly states when to use the tool ('Use for cross-platform social-presence checks'), giving clear context. However, it does not provide explicit when-not-to-use scenarios or alternatives, though the sibling list implies the 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?

With no annotations, the description carries full burden. It states 'Get' implying read-only but does not confirm non-destructive nature, mention authentication, rate limits, or error handling. Cost is mentioned, but behavior beyond data retrieval is unclear.

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

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

The description is two sentences plus a cost note, front-loaded with core purpose and immediate usage detail. Every sentence is informative with no wasted words.

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

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

Given no output schema and no annotations, the description covers core functionality, parameter format, and cost. It omits response structure and error cases, but these are reasonable omissions for a simple enrichment tool.

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

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

The schema has 0% coverage, so the description compensates by specifying the username format ('without the @ sign'), which adds value beyond the schema's simple string type.

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

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

The description clearly states the tool retrieves TikTok profile data including followers, bio, verified status, and post stats. It distinguishes itself from sibling tools like enrich_instagram or enrich_linkedin by specifying the platform.

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 like search_tiktok_hashtag or get_recent_tweets. The description lacks context for appropriate usage 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?

No annotations provided, so the description carries the burden. It discloses cost ($0.05) and indicates read behavior (get), but does not mention rate limits, authentication, or other edge cases. Adequate but not thorough.

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

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

Extremely concise: three short sentences covering purpose, argument, and cost. No redundant information. Every part is useful and front-loaded.

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

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

For a simple one-parameter tool with no output schema, the description is fairly complete: what it returns, input format, and cost. Missing details like return structure or pagination are not critical. Slightly benefits from sibling context.

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

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

The input schema has 0% description coverage, so the description adds value by specifying the username format ('without the @ sign'). This clarifies usage beyond the schema's type-only definition. Could also mention case sensitivity or length limits.

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 retrieves specific fields (followers, bio, verified status, post count) from an X/Twitter profile. The verb 'Get' and named resource 'X/Twitter profile' are specific, and it distinguishes from siblings like enrich_instagram and enrich_linkedin by platform.

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 X/Twitter profiles, and sibling tool names clarify platform distinctions. However, it lacks explicit when-to-use or when-not-to-use recommendations, e.g., for recent tweets use get_recent_tweets.

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

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

No annotations are provided, so the description must fully convey behavioral traits. It mentions cost ($0.005–$0.05 USDC per call) and supported formats, but omits critical details such as whether the operation is read-only, error handling behavior, rate limits, authentication requirements, or return value structure. The lack of output schema or description of what 'structured data' entails leaves significant 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?

The description is tightly written with no superfluous content. The purpose statement, supported formats, use cases, example call, and cost are each presented concisely and in logical order. Every sentence contributes necessary information without redundancy.

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

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

The tool involves document extraction with no output schema, so the description should ideally clarify output format, error handling, and limitations. It covers purpose, parameter usage, supported types, and cost. However, it does not describe the structure of returned data, what happens when extraction fails, or whether there are size or content constraints. This leaves some gaps for an AI agent to reason about tool 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?

With schema description coverage at 0%, the description must compensate. It explains that the query_string parameter should include a URL (e.g., 'url=https://example.com/invoice.pdf') and shows an example call. While the parameter format (URL encoding) is not explicitly stated, the example provides sufficient guidance for an AI agent to construct valid input. This adds meaningful context beyond the bare schema.

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

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

The description clearly states the tool's function: extracting structured data (tables, forms, invoice fields) from document URLs. It specifies supported formats (PDF/PNG/JPG) and use cases (invoice processing, form parsing, document AI). This distinguishes it from sibling tools, which are primarily web scrapers or lookups, 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 provides context for using the tool: 'Use for invoice processing, form parsing, document AI.' It does not explicitly state when not to use or compare to alternatives, but the examples and format constraints imply it is specifically for document extraction, not general webpage scraping. The guidance is clear but lacks 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?

No annotations are provided, so the description carries full burden. It describes a read-only retrieval operation (returning snippets) with no side effects, but does not disclose any behavioral traits such as idempotency or required permissions. The description is 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?

Single sentence, to the point, with no unnecessary words. Every word earns its place, listing all supported platforms 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?

For a zero-parameter tool with no output schema, the description is sufficiently complete. It specifies the output (snippets) and target platforms. Slight lack of detail on snippet format, but adequate for intended use.

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

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

There are zero parameters, so schema coverage is 100%. Per calibration, baseline is 4 for no parameters. The description adds value by listing the platforms covered, which is contextually useful.

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 returns ready-to-paste configuration snippets for installing the MCP server in specific tools. The verb 'Return' and resource 'configuration snippets' are specific, and it distinguishes from sibling tools which are all lookup/scrape/enrich functions.

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 needing installation snippets, but does not explicitly state when to use or not use this tool, nor provide alternatives. Given the context of sibling tools, usage is reasonably implied.

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

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

With no annotations, the description carries full burden. It accurately describes the tool as a read-only query operation with no side effects. It could be more detailed about return format, but for a simple list operation, it is sufficiently transparent.

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

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

The description is exceptionally concise, comprising two short sentences. The first sentence front-loads the core purpose, and the second provides the key behavioral point (free call). No extraneous 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 parameterless tool, the description covers the main purpose and cost aspect. It does not detail the output format, but given the simplicity and lack of output schema, the description is reasonably complete for an agent to understand the tool's function.

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 schema coverage is 100% (empty). The description does not need to add parameter details. Baseline of 4 is appropriate as no additional parameter information is necessary.

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: listing all available paid endpoints and their current prices. It distinguishes itself from sibling tools that focus on data enrichment or social media searches, making its role as a directory query 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 explicitly mentions that the tool is a free call requiring no payment, providing clear context for when to use it. However, it lacks explicit guidance on when not to use it or alternatives, though the sibling tool names imply differentiation.

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

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

No annotations are provided, so the description must fully describe behavior. It mentions cost, which is useful, but does not disclose whether the call is read-only, idempotent, or any side effects. The 'random' nature is stated but not elaborated.

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

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

Two sentences plus a cost note make the description efficient and front-loaded. The essential purpose is captured in the first sentence, with additional context in the second. 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?

For a simple tool with one optional parameter and no output schema, the description is minimal. It lacks details on what the advice content looks like, how the query_string affects results, or any limitations, leaving gaps for an agent.

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

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

The input schema has 0% parameter description coverage, and the description does not explain the 'query_string' parameter. The agent must infer its purpose from the name alone, which is insufficient for correct usage.

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

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

The description clearly states 'Get a random piece of advice' with a specific verb and resource. It distinguishes from sibling tools like lookup_joke and lookup_random_quote by focusing on 'advice', making its 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 provides explicit guidance: 'Use for content-fill or personal-assistant agents.' This gives clear context for when to use the tool, though it does not mention exclusions or alternatives to similar lookup tools.

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

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

The description adds cost information ($0.005–$0.05 USDC per call), which is a useful behavioral trait not covered by annotations (since none exist). It does not specify whether the operation is read-only, idempotent, or if there are rate limits or side effects. Given no annotations, the description carries the burden but only partially fulfills it.

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: purpose, use case, and example with cost. It is concise, front-loaded with key information, and contains no extraneous text.

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

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

For a simple tool with one parameter and no output schema, the description covers input format, purpose, use case, example, and cost. The output format ('years/months/days') is mentioned but not precisely specified; however, it is sufficient for an agent to infer the result structure.

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

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

The description adds meaning beyond the input schema by specifying the required format (YYYY-MM-DD) and providing an example. This compensates for the schema's 0% description coverage, as the schema only defines 'birthdate' as a string without format constraints.

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

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

The description clearly states the tool calculates age from a birthdate, specifying the output units (years/months/days) and the input format (YYYY-MM-DD). The verb 'calculate' and resource 'age' are specific, and the tool distinguishes itself from siblings like 'lookup_country' by its unique function.

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

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

The description explicitly recommends use for 'HR and registration agents', providing a clear context. However, it doesn't mention when not to use the tool or suggest alternatives, which slightly limits 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?

Discloses cost ($0.005–$0.05 USDC per call), which is useful behavioral info, but lacks details on rate limits, error handling, or data freshness. No annotations are present to supplement.

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 covering purpose, usage context, example, and cost. Front-loaded and free of unnecessary information.

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

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

Sufficient for a simple tool with one parameter and no output schema: includes purpose, usage guidance, an example, and cost. Slight gaps in behavioral transparency but overall adequate.

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

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

Example call with 'frieren' adds practical meaning to the query parameter, compensating for the lack of schema description (0% coverage). However, no further parameter details are provided.

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 'Search AniList for anime/manga metadata' with a specific verb and resource, and is distinct from sibling tools focused on other 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?

Specifies use for 'anime-recommendation and otaku-content agents', providing clear context, but does not explicitly mention when not to use it or name alternatives.

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

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

No annotations are provided, so the description carries full burden. It discloses cost and output fields but lacks details on result limits, sorting, freshness, rate limits, authentication, or error behavior. The mention of 'recent' is vague.

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—three short sentences plus an example. It front-loads the main action, includes an example call, and appends cost info. Every sentence adds value.

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

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

For a simple one-parameter tool with no annotations or output schema, the description covers purpose, example, and cost. However, it omits result set size, sorting, authentication, and error handling, which are important for an agent to use it reliably.

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

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

Schema coverage is 0%, so the description must compensate. It defines the query parameter as a search string and provides an example, but does not specify format, length limits, or advanced search syntax. Minimal added meaning beyond the schema.

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

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

The description clearly states the action ('Search arXiv') and resource ('recent papers'), and specifies the fields returned (title, authors, abstract, PDF link). It distinguishes itself from sibling lookup tools by targeting a specific domain (arXiv).

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

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

The description explicitly states when to use this tool ('for ML/AI research agents and literature review'). It does not mention when not to use it or alternatives, but the context is clear for agents.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions cost and an example call but does not indicate whether the operation is read-only, what happens on errors, or response details beyond listed fields.

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

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

Three sentences: purpose, use case example, and cost. The information is front-loaded and every sentence adds value with no redundancy.

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

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

For a simple lookup with one parameter and no output schema, the description lists return fields and cost but lacks details on error handling or response structure. It is minimally adequate but could be more complete.

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

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

The schema has 0% description coverage, so the description must compensate. It provides an example call ('{"asn": "AS15169"}') that implies the format (AS plus number), adding partial value beyond the schema, but lacks full semantic explanation.

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: 'Get ASN metadata (org, country, CIDR ranges).' It specifies the resource (ASN) and the returned fields, and distinguishes itself from sibling lookup tools by being ASN-specific.

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

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

The description advises using the tool for 'network-research and threat-intel agents,' providing clear context. However, it does not explicitly exclude cases or compare to alternatives like lookup_ip or lookup_dns.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions encoding/decoding and includes a cost range, but does not describe error handling, output structure, or potential side effects.

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

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

The description is very concise with no unnecessary words. It provides the core information and an example in just three sentences, earning 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?

No output schema exists, so the description should at least hint at what the tool returns (e.g., encoded/decoded string). It lacks this information, making it incomplete for agent decision-making.

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

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

With 0% schema coverage, the description compensates by explaining the query string format (text parameter and op with encode/decode). The example clarifies usage. However, it could be more explicit about parameter constraints.

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

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

The description explicitly states 'Encode or decode base64', specifying the verb and resource. It distinguishes from sibling lookup tools by its unique function.

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

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

The description includes 'Use for data-format agents', which is vague and does not provide clear guidance on when to use this tool versus alternatives like lookup_url_encode or lookup_hash.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions cost per call ($0.005–$0.05 USDC), which is beneficial, but lacks disclosure on idempotency, error handling, or whether it requires authentication. The tool is a search, so likely read-only, but this is not 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 (three sentences) and front-loaded with purpose. It includes a clear example and cost information. However, it could be slightly more structured by separating the example and cost into distinct sections.

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

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

For a simple single-parameter tool, the description covers purpose, use case, and cost. However, it does not describe the output format or any limitations (e.g., result count, pagination). Given no output schema, this omission leaves the agent guessing about the return structure.

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

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

Schema description coverage is 0%, so the description should compensate. It only provides an example call with a 'query' parameter but does not explain the parameter's meaning, format, or constraints. The description merely implies it's a search string without further elaboration.

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 searches craft-beer metadata and lists specific fields (name, brewery, abv, ibu, style). It distinguishes from the many sibling lookup tools by focusing on a specific domain and providing a use case for hospitality and food 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 provides a concrete use case ('Use for hospitality and food agents'), guiding the agent on appropriate contexts. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as authentication requirements, rate limits, or side effects; it only mentions cost, which is not a behavioral detail.

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

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

Description is very concise, using two sentences plus an example and cost note, effectively front-loading the key information.

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

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

Covers core functionality and use case but lacks details on return value structure, error handling, and behavior when handle is not found; no output schema exists to compensate.

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

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

With 0% schema description coverage, the description provides only an example handle ('jay.bsky.team') without explaining the format or purpose of the 'handle' parameter.

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 retrieves a Bluesky profile with specific fields (followers, bio, post count, avatar) and identifies a use case ('emerging-platform creator research'), distinguishing it from sibling lookup tools for other platforms.

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

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

Provides a clear use case context but lacks explicit guidance on when not to use or alternatives; however, the specialization to Bluesky implicitly differentiates it from siblings.

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

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It states the cost ($0.005–$0.05 per call), which is useful, but does not mention auth requirements, rate limits, or whether the tool is read-only (implied but not explicit). Safety aspects are partially addressed but incomplete.

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

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

The description is concise with three sentences: purpose, use case, and example with cost. It is front-loaded with the primary purpose. Every sentence adds value with no redundancy, though it could be slightly tighter by merging example and cost.

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 no output schema and no annotations, the description should clarify what the output looks like. It mentions categories (frameworks, analytics, CMS, hosting) but does not specify the return format (e.g., JSON structure). The tool is simple, so completeness is adequate but has a gap in output description.

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

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

The description includes an example call with 'domain': 'stripe.com', but it does not clarify the expected format (e.g., full URL vs. just the domain name). Since the input schema has 0% description coverage, the description should compensate, but it adds minimal semantic value beyond the schema itself.

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 detects a website's tech stack including frameworks, analytics, CMS, and hosting. It gives a specific verb ('detect') and resource ('tech stack'), and distinguishes from sibling lookup_ tools that focus on other data (e.g., IP, DNS). Use cases for competitive analysis and lead enrichment add clarity.

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

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

The description explicitly mentions 'Use for competitive analysis and lead enrichment', providing clear context for when to use this tool. However, it does not discuss when not to use it or offer explicit comparisons to similar siblings like lookup_similarweb, so it lacks full exclusions.

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

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

With no annotations, the description carries full burden. It discloses cost ($0.005–$0.05 USDC on Base) and includes an example call, but does not mention rate limits, authentication, or any side effects. This is adequate but not thorough.

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

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

The description is concise: three sentences plus an example line. It front-loads the purpose, adds usage context, and includes a concrete example. No unnecessary words.

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

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

Given a simple single-parameter lookup tool with no output schema, the description provides purpose, expected return fields (price, mcap, etc.), an example, and cost. It is fairly complete, though it could briefly mention that the response contains the listed 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?

The input schema has 0% coverage (no description for coin_id). The description only provides an example ('bitcoin') without explaining valid values, formats, or whether it expects coin names vs IDs. This adds minimal value beyond the schema.

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

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

The description explicitly states it provides 'Detailed CoinGecko metadata for a coin (price, mcap, volume, links, dev activity)', which clearly identifies the resource and the specific verb (lookup). It distinguishes from sibling lookup tools by specifying CoinGecko and crypto context.

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 'Use for crypto-research agents', providing clear context. However, it does not explicitly mention when not to use this tool or alternatives, leaving some ambiguity among the many lookup siblings.

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

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

No annotations given; description provides cost but does not disclose that it is a read-only, idempotent operation or any potential side effects.

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

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

Description is concise (3 lines) and front-loaded with purpose; every sentence adds value.

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

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

Covers purpose, parameter format, and cost, but does not describe the output (e.g., ratio value) or any return structure, which 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 has 0% parameter description; the description explains the required format ('FG/BG' without '#') and provides an example, compensating effectively.

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

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

The description states a specific verb ('Calculate') and resource ('WCAG color-contrast ratio'), distinguishing it from sibling tools like lookup_color_palette.

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

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

Description indicates use for accessibility and design agents, providing context, but does not explicitly state when not to use or mention alternatives.

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

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

No annotations are provided, so the description carries the full burden. It mentions the cost range, which is good, but lacks details on side effects, permissions, or behavioral traits like idempotency. The cost disclosure adds value, but other aspects are missing.

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

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

The description is three concise sentences plus an example and cost line. It is front-loaded with the primary function and use case, with no redundant information. Every sentence serves a purpose.

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

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

Given the low complexity (1 parameter, no output schema), the description covers purpose, example, and cost. However, it does not describe the return structure (e.g., array of hex codes), which is needed for an agent to properly use the 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 schema has 0% description coverage, so the description must compensate. It does so by explaining 'seed hex code' and providing an example ('3b82f6'), giving the agent enough context to understand the parameter's purpose and format.

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

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

The description clearly states the tool generates a complementary color palette from a seed hex code, with a specific use case for design agents and theme generators. It distinctly differs from siblings like lookup_color_contrast.

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 including an example call and stating the intended use. However, it does not explicitly mention when not to use the tool or list alternatives.

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

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

Without annotations, the description provides cost and an example but does not explicitly state that the tool is read-only, safe, or has no side effects. The cost disclosure adds some transparency, but more behavioral details (e.g., idempotency, auth) would improve.

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

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

Two sentences plus example and cost. No extraneous information; front-loaded with action and key details. Highly efficient.

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

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

Covers purpose, input example, output fields, and cost. Lacks specification of response format (e.g., JSON structure) but sufficient for a simple lookup tool with one parameter.

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

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

Only one parameter with 0% schema coverage. The description adds an example ('japan') and indicates it's a string, but does not clarify format (e.g., country name vs code, case sensitivity). Baseline for 0 params is 4, but with one param, 3 is reasonable for adding context.

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

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

Clearly states 'Get country metadata' with specific data fields, example input, and cost. Distinguishes from sibling lookup tools by focusing on country data and use cases (localization, finance, travel).

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

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

Explicitly says 'Use for localization, finance, and travel agents,' providing context. Does not explicitly mention when not to use or alternatives, but the specificity implies when it's appropriate.

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

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

No annotations provided, so description bears full responsibility. It mentions pricing but does not disclose error handling (e.g., if crate not found), response format, or any side effects. This is insufficient for a production 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?

Three concise sentences: purpose, example, cost. Front-loaded with the core function, no extraneous text. Every sentence adds value.

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

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

For a one-parameter tool, the description covers purpose, usage context, and cost. However, lacking details on output format or error behavior (e.g., not found) leaves gaps in completeness, especially with no output schema.

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

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

The only parameter 'pkg' has no schema description (0% coverage), but the description adds an example ('tokio') and specifies it refers to a Rust crate name, giving clear semantics beyond the raw schema.

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

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

The description clearly states the tool retrieves crates.io metadata for Rust crates, specifying the resource (crates.io) and action (get metadata). It distinguishes from sibling lookup tools by targeting Rust ecosystem.

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

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

Explicitly suggests use for Rust-dependency research and provides an example call, aiding agent selection. However, it does not explicitly state when not to use this tool (e.g., for non-Rust packages), though the naming and context imply the domain.

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 behavioral traits. It mentions validation and network detection but omits details on error handling, response format, or any side effects. The cost information is a plus but not strictly behavioral.

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

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

The description is concise: three sentences covering purpose, usage, example, and cost. No redundant information, and the key details 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?

For a simple tool with one parameter and no output schema, the description covers purpose, parameter format, usage context, and cost. It lacks details on return values or error behavior, but the core functionality is adequately explained.

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

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

The input schema has 0% description coverage, but the description compensates by explaining that the query_string should contain 'number=...' and provides an example. This clarifies what would otherwise be an opaque string parameter.

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's purpose: 'Luhn-validate a credit-card number and detect the network.' This clearly identifies the verb+resource combination and distinguishes it from other sibling tools with similar prefixes.

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 usage context with 'Use for fintech UX agents (NOT a fraud check).' This helps agents decide when to invoke the tool and when to avoid it. However, it does not mention explicit alternatives for fraud checking.

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 adds cost information ($0.005–$0.05 per call) but does not disclose rate limits, authentication requirements, error handling, or whether the call is destructive. The cost detail is helpful but incomplete for a full behavioral profile.

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

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

The description is succinct: one sentence for purpose, one sentence for use cases, an example, and cost. All sentences add value, no wasted words. Information is front-loaded.

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

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

For a simple lookup with one parameter and no output schema, the description covers the essential: what it returns, source, example, and cost. It lacks explicit response format details, but the example implies a JSON response. Given no output schema, this is adequate.

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

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

The schema has one string parameter (symbol) with no description. The tool description adds meaning by listing example symbols (BTC, ETH, SOL) and providing an example call, clarifying acceptable values. This compensates for the 0% schema description 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 retrieves live crypto price and 24h change for symbols like BTC, ETH, SOL. It specifies the source (CoinGecko) and suggests use cases. This distinguishes it from siblings like lookup_coingecko by focusing on price data.

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

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

The description mentions use cases (portfolio agents, trading bots, DeFi research), providing context on when to use. However, it does not explicitly state when not to use or compare with alternative tools like lookup_coingecko or scrape_coinbase.

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

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

No annotations are provided, so the description must disclose all behavioral traits. It mentions cost ($0.005–$0.05 per call) and implies a read operation, but fails to disclose authentication requirements, rate limits, error handling for invalid dates, or whether the call is idempotent. Significant gaps remain.

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 with only three sentences and one example line. It front-loads the purpose, follows with usage context, and includes an example and cost in a clear structure. No wasted words; every sentence adds value.

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

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

Given no output schema, the description should explain return format (e.g., rate, currency codes). It also lacks error behavior, authentication, and rate limit info. While simple, the tool is part of a large sibling set with similar functions, and more context would help agents decide 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 'base_target_date' has no schema description (0% coverage). The description clarifies its format: 'base/target/date (YYYY-MM-DD)' and provides an example ('USD/EUR/2024-01-15'), which adds meaningful context beyond the parameter name. Could further specify date validity constraints.

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

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

The description clearly states the tool returns a historical FX rate with required input format. However, it does not explicitly differentiate from the sibling tool 'lookup_exchange', which may also provide exchange rates. The example with USD/EUR implies fiat currencies, but explicit distinction is missing.

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 use cases ('accounting backfill or historical-analysis agents') but lacks exclusions (e.g., not for real-time rates) and does not mention alternatives like lookup_exchange. Guidance is present but incomplete.

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 present, so the description carries the full burden of behavioral transparency. It does not disclose side effects (e.g., external API calls, rate limits, or idempotency). The cost information is helpful but does not address core behavioral traits. The description focuses on output components, not the operation's nature.

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

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

The description is extremely concise: two sentences plus an example and cost note. It front-loads the purpose and includes essential details without extraneous information. Every element 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 one-parameter tool with no output schema, the description covers purpose, example, cost, and target audience. It lists returned components, which partially compensates for the missing output schema. However, it lacks details on error handling, input validation, or behavioral traits, leaving some gaps.

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

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

The input schema has 0% description coverage for the parameter 'cve_id'. The description only provides an example ('CVE-2021-44228'), which suggests a format but does not formally define constraints, patterns, or semantics. It adds minimal value beyond the schema's type declaration.

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: 'Look up a CVE' and lists the returned data (description, CVSS, references, affected products). It also specifies the target audience ('use for security-monitoring agents'), which differentiates it from many other lookup_* sibling tools that cover different domains.

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

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

The description provides a clear usage context ('use for security-monitoring agents'), guiding the agent on when to apply this tool. It does not explicitly mention when not to use it or list alternatives, but given the large number of sibling tools, the specific mention of security monitoring is sufficient to set expectations.

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

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

With no annotations, the description must disclose all behavioral traits. It adds cost information ($0.005–$0.05 per call) and example output types, but lacks details on error handling, language scope (English only implied), rate limits, or authentication requirements.

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

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

The description is extremely concise with three lines plus an example and cost. It front-loads the core purpose and uses bullet-like structure for easy scanning. No extraneous 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 single-parameter tool, the description provides the essential purpose, example, and cost. However, it lacks output details (no output schema) and does not cover edge cases or error scenarios, making it less complete than ideal.

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

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

Schema coverage is 0%, so the description must compensate. It clarifies the 'word' parameter is the English word to look up, and the example reinforces its usage. However, it does not specify constraints like case sensitivity or validity, leaving some ambiguity.

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

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

The description clearly states it retrieves dictionary definitions for an English word, listing specific content (meanings, examples, phonetics). It provides an example call, distinguishing it from sibling lookup tools like lookup_translate or lookup_wikipedia.

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

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

It mentions 'Use for writing and language agents,' giving a usage context, but does not explicitly contrast with alternatives such as lookup_translate for translation or lookup_wikipedia for encyclopedia entries. No when-not-to-use guidance is 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?

No annotations are provided, so the description must carry the full burden. It discloses the cost ($0.005–$0.05 per call) and that it returns multiple record types, but does not mention error handling, rate limits, or data freshness. The description adds some behavioral context but is not comprehensive.

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

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

The description is very concise: three sentences covering purpose, use cases, example input, and cost. No unnecessary words; all sentences are value-adding.

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?

While the description gives use cases and cost, it does not describe the output structure. With no output schema provided, the agent must guess what the response looks like. For a tool returning multiple DNS record types, this is a significant gap.

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 'domain' has 0% schema description coverage. The description adds an example call with 'github.com', which clarifies the expected format (plain domain without protocol or slashes). This goes beyond the schema, but does not explain validation rules or constraints.

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

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

The description clearly states it resolves DNS records for a domain and lists specific record types (A, AAAA, MX, TXT, NS). This is a specific verb+resource that distinguishes it from siblings like lookup_mxrecords which only handle MX records.

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

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

The description provides explicit use cases: security audits, email-deliverability checks, and infra discovery. However, it does not specify when not to use it or compare with related siblings (e.g., lookup_mxrecords for only MX lookups).

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

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

No annotations are provided. The description discloses cost and example call, but lacks details on rate limits, authentication, error behavior, or response format. Some transparency but incomplete.

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

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

Three sentences covering purpose, example, and cost. Each sentence adds unique value, front-loaded with key information. No redundancy.

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

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

For a simple tool with one parameter and no output schema, the description covers core aspects but misses details like how the image parameter should be formatted and how this differs from scrape_dockerhub. Adequate but could be more thorough.

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

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

Schema coverage is 0% for the single parameter 'image'. The description gives an example ('library/postgres') but does not explain the expected format (e.g., namespace/repo, with or without tag). Adds minimal 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?

The description clearly states the action (get metadata for Docker Hub images) and lists specific data elements (last push, pull count, tags, size). It differentiates itself from siblings like scrape_dockerhub by focusing on 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 mentions use cases (container audits, supply-chain research) and provides an example, but does not explicitly contrast with alternatives (e.g., scrape_dockerhub) or state when not to use. Guidelines are implicit.

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 cost per call ($0.005–$0.05 USDC on Base) and gives an example call. However, it does not mention whether the operation is read-only, rate limits, or return format.

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

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

Three sentences: purpose, usage context, example, and cost. No redundant information, front-loaded with key 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 has one parameter and no output schema. The description provides purpose, context, example, and cost, which is sufficient for a simple lookup tool. It could be improved by detailing what 'info' includes, but it is mostly 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 schema has 0% description coverage. The description only gives an example call with 'shiba' but does not explain what the breed parameter accepts (e.g., common names, scientific names). It partially compensates with the example but lacks full guidance.

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

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

The description clearly states 'Get info + image for a dog breed.' This is a specific verb+resource combination that distinguishes this tool from the many other lookup tools in the list.

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

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

It says 'Use for pet content agents,' providing a clear context. While it doesn't explicitly exclude alternatives, the narrow scope of dog breeds makes it clear when to use 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 must carry the full burden. It discloses the cost per call, which is good, but does not mention error handling (e.g., invalid domain), rate limits, or whether the tool is idempotent. The return values (creation date, age) are stated but lack format 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 three sentences: purpose, example call, and cost. It is front-loaded with the core action and use cases. No superfluous words, and the structure is efficient for an agent to quickly understand.

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

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

For a tool with one parameter and no output schema, the description provides enough: input example, output summary (creation date and age), and cost. It could be improved by noting the output format or error cases, but overall it sufficiently informs an agent 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 schema has only one parameter ('domain') with no description. The description provides an example call ('{"domain": "google.com"}'), which implies a domain string without protocol. However, it does not specify allowed formats (e.g., with/without www, subdomains). With 0% schema coverage, the description adds value but is not fully explicit.

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 gets a domain's age (creation date, age in years) and explicitly mentions use cases (trust scoring, SEO research). The verb 'Get' and resource 'domain age' are specific, and the name and description distinguish it from sibling lookup tools.

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

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

The description gives example usage and mentions use cases, but does not explicitly state when NOT to use this tool or provide alternatives. Given the many sibling tools, some guidance on exclusion would help but is not critical for this simple lookup.

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 that it performs syntax and MX-record checks and includes cost, but no details on return format (e.g., boolean or details) or any rate limits. Without annotations, this is adequate but not exhaustive.

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

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

Very concise: two sentences, an example, and cost. Front-loaded with purpose. Every sentence adds value.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose, usage, cost, and example. Lacks return value description, but overall 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 is 0%. Description only provides an example call, adding minimal meaning beyond the schema's type and title. More detail about expected format or constraints would improve this.

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 validates email using syntax and MX records, with a specific use case for lead-list cleaning. This distinguishes it from sibling tools like lookup_mxrecords.

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

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

Explicitly says 'Use for lead-list cleaning before sending cold email,' providing clear context. Lacks explicit when-not-to-use, but the use case is well-defined.

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

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

No annotations are provided, so the description must cover behavioral aspects. It mentions cost per call ($0.005–$0.05 USDC) but does not disclose whether the operation is read-only, destructive, or any side effects. Missing details like error handling or rate limits.

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

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

The description is very concise (3 sentences), front-loading the purpose, then showing an example and cost. Every sentence adds value, and the structure is optimal for quick comprehension.

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 (single parameter, no output schema, no annotations), the description provides essential information: purpose, example, cost. It lacks return format details or error handling, but for a basic lookup tool, it is mostly 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 single parameter 'query' has 0% schema description coverage. The description adds meaning by explaining it is a keyword for searching emojis and provides an example ('fire'). This clarifies usage beyond the simple type definition.

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: 'Search emojis by keyword' and specifies the return fields (unicode, shortcode, category). It also contextualizes usage for 'content-generation agents,' distinguishing it from other lookup tools that target different domains.

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

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

The description advises use for 'content-generation agents,' providing a clear context. However, it does not explicitly state when to avoid using this tool or mention alternatives among the many sibling lookup tools, which limits guidance for tool 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; description discloses cost but not other traits like authorization or rate limits. Adequate but not comprehensive for a fully burdened description.

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

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

Two sentences plus example and cost in a natural layout. Front-loaded with purpose; every element serves a purpose without redundancy.

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

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

For a simple two-param tool with no output schema, description covers action, parameters, usage context, and cost. Lacks return format info, but use case is straightforward.

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?

Despite 0% schema coverage, description adds meaning by specifying parameter type (ISO currency codes) and providing an example. Could list more sample values but covers essential semantics.

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

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

The description clearly states the tool gets a live FX rate with specific verb-resource combination, mentions 3-letter ISO codes, and distinguishes use cases. It stands out among many lookup_ tools.

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

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

Provides explicit use cases (pricing localization, accounting, finance) and an example call. Lacks explicit when-not-to-use or alternatives, but sibling tools don't directly overlap.

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 return data (nutrition, ingredients, brand) and cost ($0.005-$0.05 USDC), but does not disclose rate limits, error handling, or read-only nature explicitly. Sufficient for a simple lookup 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?

Three sentences covering purpose, example, and cost. No redundancy, every sentence adds value. Extremely efficient 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 one-parameter lookup with no output schema, the description adequately explains what it returns (nutrition, ingredients, brand) and includes cost. No missing critical information for an agent to decide 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?

Only one parameter 'barcode' with no schema description. Description adds value by specifying 'UPC/EAN barcode' format and providing an example call, clarifying input expectations beyond the schema's minimal type 'string'.

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 the verb 'look up', resource 'food product', data source 'Open Food Facts', and return types. Distinguishes from numerous sibling tools by its specific domain (food barcodes) and mention of grocery/dietary use cases.

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

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

Provides use-case suggestions ('grocery, dietary, or scanning agents') but lacks explicit when-not-to-use or alternative tools. Implicitly differentiated by domain but no direct exclusion.

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

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

No annotations are provided, so the description carries full burden. It discloses cost and the query parameter format, but lacks details on response format, rate limits, or data freshness. For a simple tool, this is 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?

The description is three sentences long, front-loads the purpose, and includes only essential information: purpose, usage, example, and cost. No superfluous 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?

Given the tool is a simple geocode lookup with one parameter and no output schema, the description covers the main aspects: purpose, usage, example, and cost. The output is implied as lat/lon. Some details like response structure are omitted, but 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?

The input schema has 0% description coverage. The description adds meaning by instructing to pass '?q=... as query' and providing an example, explaining how to format the single parameter.

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 'Forward-geocode an address to lat/lon', which is a specific verb and resource. It distinguishes from sibling tool 'lookup_reverse_geocode' by specifying forward direction.

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

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

The description provides a use case ('mapping and logistics agents') and an example call. However, it does not explicitly contrast with siblings or state when not to use it.

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

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

No annotations are provided, leaving the description to carry the burden. It mentions cost but fails to disclose whether the operation is read-only, requires authentication, or has any side effects. This omission leaves significant behavioral gaps for an 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 brief (3 sentences plus example and cost note), with no wasted words. However, the lack of bullet points or structured formatting slightly hinders quick scanning.

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

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

Given no output schema, the description lists key return fields but not the full structure. Missing details on error responses or pagination make it adequate but not comprehensive for a simple lookup tool.

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

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

With 0% schema description coverage, the description adds value via the example call clarifying owner and repo, but does not explicitly define these terms beyond the schema titles. It compensates partially but could be more explicit about parameter roles.

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

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

Description clearly states it retrieves GitHub repo metadata (stars, language, license, dates, default branch) and lists specific use cases (OSS research, dependency-risk scoring, maintainer outreach), distinguishing it from other GitHub-related siblings which focus on audits, enrichment, or specific resources.

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 use cases but does not provide explicit guidance on when not to use this tool or how it compares to alternatives like enrich_github or audit_github. The example call is helpful but falls short of full 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 provided, so the description must compensate. It mentions the tool returns specific fields and includes a cost estimate. However, it omits authentication requirements, rate limits, or whether the gist must be public. Adequate but not comprehensive.

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

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

Three sentences covering purpose, example, and cost. No wasted words. The structure is front-loaded with the most critical 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?

For a simple lookup tool with one parameter and no output schema, the description covers basic usage, return fields, and cost. However, it lacks details about output format, error scenarios, or limitations. Minimally complete but could be enhanced.

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

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

Schema has 0% description coverage on the only parameter. The description provides an example but does not explain what constitutes a valid gist ID, its format, or any constraints. The example is helpful but insufficient to fully understand the parameter semantics.

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

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

Clearly states 'Get a GitHub Gist' and lists returned fields (files, owner, description). Distinct from siblings like lookup_github_user or lookup_github_releases by specifying the resource as a gist by ID.

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

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

Provides explicit usage context: 'Use for snippet retrieval and code-research agents.' Does not include exclusions or comparisons to alternatives, but the context is clear enough for an agent to decide when to use it.

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

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

No annotations are provided, so the description must disclose behaviors. It mentions monetary cost ($0.005–$0.05 per call) but does not clarify whether the tool is read-only, requires authentication, or how many releases are returned. The cost information adds value but leaves 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?

Three sentences cover purpose, usage context, example, and cost. No redundant information; every sentence adds value.

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

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

The tool is simple with one parameter and no output schema. The description covers purpose, parameter format, example, and cost. It hints at output fields. For a straightforward lookup, this is nearly complete; only missing a note on pagination or result count.

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

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

Schema description coverage is 0%, but the description adds meaning by explaining the parameter format ('Pass owner/repo') and providing an example ('vercel/next.js'). This compensates well for the lack of schema-level descriptions.

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

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

The description clearly states the tool lists recent GitHub releases for a repo, specifying fields (tag, name, body, published). This verb+resource combination distinguishes it from sibling lookup tools like lookup_github and lookup_github_gist.

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

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

The description explicitly recommends use for changelog and dependency-update agents, providing clear context. It does not list when not to use or alternatives, but the purpose is well-scoped compared to siblings.

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

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

With no annotations, the description carries the full burden. It discloses cost ($0.005–$0.05 USDC per call) and includes an example, but does not mention rate limits, authentication requirements, or error handling (e.g., user not found).

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

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

The description is succinct and well-structured: purpose, example, cost. Every sentence adds value with no redundancy.

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

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

For a simple one-parameter tool with no output schema, the description covers the main purpose, provides an example, and lists returned data fields (repos, followers, etc.). However, it could mention what happens if the username is invalid or 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?

Schema coverage is 0%, so the description should compensate. The example call ('{"username": "torvalds"}') implicitly illustrates the parameter, but there is no explicit description of the username field or its expected format beyond what the schema 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 it retrieves a GitHub user's public profile, listing specific data fields (repos, followers, bio, hireable). It distinguishes itself from sibling tools like lookup_github (which may focus on repos) by explicitly targeting user profiles.

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

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

The description provides explicit use cases ('recruiter and developer-lead research'), giving context for when to use it. However, it does not mention when not to use it or how it differs from similar siblings like enrich_github or lookup_github.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost range ($0.005–$0.05 USDC), which is helpful for a paid tool. However, it does not mention error behavior when a module is not found or any prerequisites, but for a simple lookup, the transparency is above average.

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

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

The description is three concise sentences: purpose, example, and cost. Every sentence adds value with no redundancy or fluff. It is front-loaded with the core purpose.

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

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

Given the tool has one parameter and no output schema, the description covers purpose, usage context, and cost. It lists the data returned (version, repo, license), which provides a useful hint about output. Minor omissions are acceptable for a simple lookup 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 schema has 0% description coverage, but the tool description compensates with an example call showing a real Go module path. This clarifies the expected format beyond the schema's bare 'string' type and 'Module' title.

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

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

The description clearly states the tool retrieves Go module metadata, listing specific fields (latest version, repo, license). The verb 'Get' and resource 'Go module metadata' are precise, and the purpose is distinct from many sibling lookup tools targeting other ecosystems.

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

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

The description explicitly says 'Use for Go-dependency audits,' providing a clear use case. While it does not mention when not to use or list alternatives, the sibling tools cover many other languages, making this guidance sufficient for typical scenarios.

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

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

No annotations provided, so description carries full burden. It discloses algorithms, input format, example call, and cost. Does not detail response format or error handling, but sufficient for a simple hashing 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?

Three sentences front-loading purpose, then input format, example, cost. No redundancy, each sentence adds value.

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

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

For a simple hashing tool with no output schema, description covers algorithms and input. Missing output details but adequate for typical use.

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

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

Input schema has one parameter with no description. The description adds significant meaning by explaining the query string format (text=...&algo=...) and providing an example, fully compensating for 0% schema coverage.

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

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

Description clearly states 'Hash a string (md5/sha1/sha256)', specifying verb (hash) and resource (string), distinguishing it from siblings like lookup_base64 which encodes.

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

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

Explicitly says 'Use for checksum and integrity agents', giving clear context. Does not mention alternatives or when not to use, but the use case is well-defined.

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

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

No annotations provided, but the description covers basic behavior (returns top stories or specific story) and cost. It does not mention rate limits, authentication, or error handling.

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

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

Extremely concise: two sentences plus an example and cost. Purpose is front-loaded with no unnecessary information.

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

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

Given no output schema, the description lists returned fields but does not specify if 'top' returns a list or a single story, nor details on error cases. Minor gaps for a simple tool.

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

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

The schema has 0% description coverage. The description clarifies that 'story_or_top' accepts 'top' for front page, implying numeric IDs for specific stories, but does not specify the ID format or valid values.

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 gets Hacker News top stories or a specific story by id, listing returned fields (title, points, comments, author). It distinguishes from other lookup tools by focusing on HN stories, but does not explicitly differentiate from siblings like lookup_reddit.

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

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

Provides usage context ('trend monitoring or HN-launch analysis') and an example call. However, it lacks explicit guidance on when not to use it or mention of alternative tools.

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

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

With no annotations provided, the description includes cost and an example call, which adds some behavioral context. However, it does not disclose read-only nature, permissions, rate limits, or other behavioral traits.

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 at about 40 words across 3 sentences, front-loads the purpose, and includes an example and cost without unnecessary fluff.

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

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

For a simple lookup tool with one parameter and no output schema, the description covers purpose, fields retrieved, example usage, and cost, making it sufficiently complete for agent selection.

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

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

The input schema has 0% description coverage, and the description does not add meaning to the parameter beyond its name. The example value 'pg' helps with format but lacks semantic details like case sensitivity or validation.

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

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

The description clearly states the tool retrieves a Hacker News user profile including specific fields (karma, about, created), and provides an example call, effectively distinguishing from sibling lookup tools.

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

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

The description explicitly mentions use for HN-poster qualification, giving a clear context. However, it does not mention when not to use or provide alternatives among the many sibling lookup tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses cost per call but does not mention authentication, rate limits, or response format. For a read-only tool, the cost info adds value, but other behavioral aspects are omitted.

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

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

The description is concise with two sentences plus an example and cost. It front-loads purpose and usage, and every sentence is useful.

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

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

Given the absence of output schema and annotations, the description covers basic purpose and parameters but does not describe the output format or error handling. Adequate for a simple tool but could be more complete.

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

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

With 0% schema description coverage, the description adds meaning by specifying country as ISO-2 code and providing an example. The year parameter format is implied but not explicitly stated.

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

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

The description clearly states the tool lists public holidays for a country and year, specifying ISO-2 country code. It distinguishes itself from sibling tools by its specific holiday domain.

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

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

The description explicitly mentions use cases like scheduling, booking, and HR/calendar agents. It provides clear context but does not specify when not to use it or compare to alternatives.

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

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

With no annotations, the description carries the full burden. It discloses cost ($0.005-$0.05) but does not mention read-only nature, rate limits, or required authentication. The cost disclosure adds value beyond basic functionality.

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

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

Three sentences, front-loaded with purpose, no fluff. Every sentence adds value: purpose, example, cost.

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 is fairly complete. It covers purpose, example, and cost. It could improve by specifying the output structure, but the decoding statement gives a general idea.

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

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

Schema coverage is 0%, so the description must compensate. It provides an example ('DE89370400440532013000') that clarifies the expected IBAN format, but does not describe the parameter's meaning or constraints beyond the name.

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

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

The description clearly states the tool validates an IBAN and decodes bank/country/account information. This specific verb-resource combination distinguishes it from siblings like lookup_email_validate or lookup_country.

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

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

The description advises use for fintech and payment agents, providing context. However, it does not explicitly state when not to use it or compare to alternatives among the many lookup tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses the returned data and cost per call, which implies it is a paid lookup. However, it does not mention read-only nature explicitly, rate limits, authentication requirements, or error handling. The blockchain cost mention may be confusing.

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

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

The description is two sentences plus an example and cost note. It front-loads the core purpose and is free of unnecessary text. Every sentence adds value: use cases, example, and pricing.

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

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

For a simple 1-parameter lookup tool with no output schema, the description covers the essentials: functionality, use cases, example, and cost. It lacks return format details and error scenarios, but is sufficient for an agent to invoke correctly.

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

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

The input schema has one required string parameter 'ip' with no description (0% coverage). The description adds an example ('8.8.8.8') and explains what data is returned, adding meaningful context. It does not specify whether IPv6 is accepted or exact format, but the example helps.

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 geolocates an IP address and lists the data fields (country, city, ISP, lat/lon, timezone). It is specific and uses active verbs. However, it does not distinguish from sibling tool lookup_ipinfo, which may have similar functionality.

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

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

The description provides use cases (log enrichment, fraud signals, geo-routing logic) and includes cost information, giving context on when to use. However, it does not mention when not to use or suggest alternatives when a different tool might be better.

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

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

No annotations are provided, so the description must cover behavioral traits. It only mentions cost but fails to disclose whether it is read-only, authentication needs, rate limits, or side effects. The read-only nature is implicit but not explicit.

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

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

The description is short and front-loaded with the core purpose. It includes an example and cost information efficiently. Minor improvement could be structuring with clear sections.

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

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

Given the tool's simplicity (1 parameter, no output schema), the description covers purpose, example, and cost. It lacks details on response format or errors, but is adequate for a straightforward lookup.

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

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

With 0% schema description coverage, the description adds an example call which implies the parameter meaning. However, it does not formally describe the parameter (e.g., acceptable formats like IPv4/IPv6), leaving room for ambiguity.

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

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

The description clearly states it provides detailed IP info including ASN, org, and abuse contact, with specific use cases for security and traffic-analysis. It distinguishes from siblings like lookup_ip by emphasizing the depth of information.

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

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

It suggests use for security and traffic-analysis agents and provides an example call, but does not explicitly exclude alternatives or state when not to use. There's no comparison with similar tools like lookup_ip.

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 a cost range but lacks details on behavior like pagination, error handling, or authentication requirements, leaving significant 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 extremely concise with two sentences, an example, and cost. Every sentence provides value, and no redundant information is present.

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 is nearly complete. It covers purpose, usage context, and cost, though it omits description of the return format or behavior when no results are found.

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

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

The schema has 0% description coverage for the query parameter. The description adds meaning by indicating it's a search term via the example, but it does not specify constraints or format, making it minimally helpful.

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

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

The description clearly states the tool searches the iTunes/App Store catalog for apps, music, and podcasts, with a specific verb and resource. It distinguishes itself from siblings like enrich_spotify, which targets a different platform.

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

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

The description advises use for app-research and music-discovery agents, providing clear context. However, it does not explicitly exclude alternatives or specify when not to use it, such as for Spotify content.

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

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

With no annotations, the description adds value by stating the joke is 'clean' and providing cost information ($0.005–$0.05 USDC). However, it does not disclose other behavioral traits like rate limits or authorization requirements.

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

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

The description is exceptionally concise with two sentences: first states purpose, second states cost. Information is front-loaded, no redundant text.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description adequately conveys core functionality but lacks details about return format or parameter behavior. Additional information would improve completeness without being excessive.

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 one parameter (query_string) with no description and 0% schema description coverage. The tool description does not mention or explain this parameter, leaving its purpose unclear. A default value of empty string suggests it might be optional, but the description fails to clarify.

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

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

The description clearly states the tool retrieves a random clean joke, with specific use cases (content-fill or conversational agents). It distinguishes itself from sibling tools which are mostly data enrichment or scraping.

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

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

The description explicitly mentions when to use the tool (content-fill or conversational agents), providing clear context for usage. No explicit exclusions or alternatives, but the context is sufficient for typical scenarios.

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

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

No annotations exist, so the description carries full burden. It mentions HTTP method (POST), cost, and that it returns errors. However, it does not disclose the response format, whether it modifies state, or require authentication. The inconsistency between the schema parameter 'query_string' and the example using 'body' reduces transparency.

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

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

The description is short and front-loaded with the purpose. However, the example is not properly formatted and does not match the schema, detracting from clarity. The cost line is useful but could be integrated better.

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

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

Given the tool's simplicity (one parameter, no output schema), the description should have clarified the parameter's role and the output format. It fails to do so, leaving the agent with a mismatch between schema and usage example.

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

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

Schema coverage is 0%, and the description does not explain the 'query_string' parameter. Instead, it provides an example with a 'body' field not present in the schema, introducing confusion. The description adds no 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?

The description states 'Validate a JSON document and return errors', which is a specific verb+resource. It also mentions 'Use for data-pipeline agents' and 'POST with JSON body', adding context. However, it does not explicitly differentiate from other validation tools in the sibling list, like lookup_email_validate.

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 'Use for data-pipeline agents', which provides some context, but does not specify when to use this tool versus alternatives or when not to use it. No exclusions or comparisons are 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?

Discloses that no verification is performed and includes cost information. Lacks details on error handling, invalid tokens, or data sensitivity. Without annotations, 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?

Three short sentences: purpose, example, cost. Every sentence adds value without redundancy. Front-loaded with 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 simple one-parameter tool, the description covers purpose, usage, and cost. Lacks response format or error handling, but the agent can infer from the decoder nature. No output schema needs to be explained.

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?

Clarifies that the query_string parameter should contain the full 'token=...' query, as shown in the example. This adds meaning beyond the schema which only provides a default empty string. High schema coverage (0%) increases the need for description, which it partially meets.

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 decodes a JWT (header and claims) without verification. It specifies the use case for auth-debug agents, distinguishing it from sibling tools which are mostly for lookups and scraping.

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

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

Provides explicit instruction to pass the token as a query string and includes an example call. However, it does not explicitly state when to use this tool over alternatives, though no sibling tool serves a similar purpose.

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 of behavioral disclosure. It does not specify read-only nature, rate limits, response format, or what 'recent' means in terms of post count or time range. The example only shows input format, not output behavior.

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: purpose, example, cost. No redundant information, front-loaded with the main action.

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

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

For a simple tool with one parameter and no output schema, the description covers the basic purpose and usage context. However, it lacks details on what data is returned (e.g., post titles, URLs) and any limitations, making it slightly incomplete.

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

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

Schema coverage is 0%, and the description does not explicitly define the 'community' parameter. However, the example 'technology@lemmy.world' provides a clear format hint. An agent can infer the expected structure, but a direct description would be more helpful.

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

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

The description clearly states the verb 'Get' and resource 'a Lemmy community's recent posts'. It is specific to Lemmy, distinguishing it from sibling tools like 'lookup_reddit' or 'lookup_mastodon'.

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 'Use for fediverse-content research', providing context. It does not explicitly state when not to use or mention alternatives, but the purpose is clear enough for an agent to decide.

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

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

No annotations are provided, so the description must carry full behavioral disclosure. It mentions the cost range ($0.005–$0.05) which is a key transparency. However, it does not disclose authentication requirements, rate limits, or the nature of the response, leaving some behavioral gaps.

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

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

The description is three sentences plus a line break, each sentence serving a distinct purpose: stating the action, providing a use case, giving an example, and listing the cost. There is no fluff, and the most critical information is front-loaded.

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

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

Despite having no output schema, the description fails to describe what the tool returns. It only says 'Lighthouse audit' but does not indicate the format (e.g., JSON scores, report). Cost info is helpful, but completeness for a data-returning tool is lacking.

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

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

The schema has 0% description coverage for the 'url' parameter, so the description must add meaning. It provides an example value ('https://example.com') and implies the parameter is a URL. This clarifies the expected format beyond the schema's type string.

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 'Run a Lighthouse audit on a URL' and lists categories (performance, accessibility, SEO, best-practices). This is a specific verb+resource, and it differentiates from siblings like 'lookup_pagespeed' which is a different web performance tool.

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

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

The phrase 'Use for web-quality agents' implies a use case but does not explicitly state when to use or when not to use, nor does it mention alternatives among the many sibling tools. The example and cost info provide context but no comparative 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?

Discloses cost range and that it generates text, but doesn't mention idempotency, limits, or error handling. With no annotations, more detail would help.

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

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

Very concise, three sentences plus example and cost, 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?

Sufficient for a low-complexity tool with one parameter and no output schema; covers purpose, usage, and cost.

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

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

Adds meaning beyond schema by explaining the parameter format (?paragraphs=N) and providing an example; schema coverage is 0% so description compensates.

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

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

Clearly states it generates Lorem Ipsum filler text for mockup and design agents, distinguishing it from sibling lookup tools that retrieve data.

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

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

Specifies use case (mockup and design agents) and gives example call, but lacks explicit guidance on when not to use or alternatives.

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

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

No annotations provided; description mentions cost but fails to disclose whether operation is read-only, requires authentication, or has rate limits. Gaps in safety and side-effect 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?

Three concise sentences: purpose, example, cost. No redundancy, information is front-loaded.

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

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

Lacks description of return value/output structure. Given no output schema, agent is left guessing what fields are returned. Cost info is helpful but not sufficient for complete understanding.

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

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

Adds meaning beyond schema by specifying 'full handle@instance' and providing example. Schema has 0% description coverage, so description compensates effectively.

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?

Clear verb 'Get' and specific resource 'Mastodon profile' with format 'full handle@instance'. Distinguishes from sibling lookup tools for other platforms.

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

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

States use case 'fediverse research' and provides example call. Does not explicitly mention when not to use or list alternatives, but context is clear among siblings.

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

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

No annotations provided, so description carries full burden. It discloses cost ($0.005–$0.05 per call) and provides an example, but lacks details on rate limits, errors, 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.