captapi

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

With no annotations, the description discloses key behavioral traits: negligible cost, cached results free, and no charge for failures. This covers cost and failure implications adequately for a balance check tool.

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

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

Two short sentences: first provides purpose with specific targets, second adds behavioral details. Extremely concise without any wasted words, front-loaded with essential information.

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

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

Given zero parameters, no output schema, and no annotations, the description fully covers purpose and behavioral context. It is complete for a simple read-only tool with no required inputs.

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 parameters, so schema coverage is 100%. The description adds no parameter-specific info, which is acceptable. Baseline for 0 params is 4.

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

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

The description clearly states the tool retrieves credit balance and plan limits, with specific verb 'Get' and distinct resource 'Captapi credit balance and plan limits'. It distinguishes from siblings like account_daily_usage by specifying unique 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 provides context on cost (~0 credits), caching (free), and failure policy (never charged), implying low-risk usage. However, it does not explicitly state when not to use or suggest alternatives like account_daily_usage for daily breakdowns.

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?

Although no annotations exist, the description discloses that calls cost ~0 credits, results are cached and free, and failures are never charged. This goes beyond a simple description by adding cost and reliability context.

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

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

Two sentences front-load purpose and add behavioral context without any fluff. Every word adds value.

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

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

For a simple tool with one parameter, the description and schema together cover purpose, cost, caching, and parameter semantics. Without an output schema, the description could briefly mention the output structure (e.g., daily breakdown), but it's nearly complete.

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

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

The sole parameter 'days' is fully described in the input schema (default 30, max 365). The description adds no extra meaning beyond what the schema already provides, meeting the baseline for high schema coverage.

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

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

Description clearly states it provides a daily request and credit usage summary. Among siblings like account_balance or account_request_history, this tool is specifically about daily usage, making its purpose distinct.

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 provides cost and caching behavior but lacks explicit guidance on when to use this tool versus alternatives (e.g., account_balance or account_request_history). Usage context is implied but not explicit.

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

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

With no annotations, the description bears full burden. It discloses cost behavior (low cost, caching, no charge for failures), which adds value. However, it omits details about data freshness, aggregation method, or whether results are historical snapshots. It covers safety aspects adequately but lacks depth.

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

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

The description is very concise: two sentences, no fluff, front-loaded with purpose. Every sentence adds value (purpose and cost context). 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?

The tool has no output schema, so description should explain return values. It says 'Most used API routes by request count and credits', giving high-level output. But it lacks detail on format, pagination, or structure. For a simple analytical tool, it is adequate but not fully complete.

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

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

Schema description coverage is 100%, so baseline is 3. The tool description does not add new parameter semantics beyond what the schema already provides (e.g., 'Billed per result' is in the schema for limit). The description relies on schema, which is sufficient.

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

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

The description clearly states the tool shows 'most used API routes by request count and credits', which is a specific verb and resource. It distinguishes from siblings like `account_daily_usage` and `account_request_history`, as it focuses on routes rather than general usage or balance.

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 cost-related context ('Costs ~0 credits; cached results are free, failures are never charged') but does not give explicit guidance on when to use this tool versus alternatives like `account_daily_usage` or `account_request_history`. No when-to-use or when-not-to-use criteria are mentioned.

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

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

No annotations are provided, so the description carries the burden. It discloses cost behavior (low cost, free cache, no charge for failures) but lacks details on time window for 'recent', pagination, or whether failed requests are included. Adequate but not comprehensive.

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

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

Two short sentences convey purpose and cost behavior with no wasted words. Information is front-loaded and easy to parse.

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

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

For a simple tool with no output schema, the description covers purpose and cost but omits expected output structure (e.g., fields returned). Missing details on how results are ordered or if they include only successful requests.

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

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

Schema coverage is 100% with clear parameter description. The description adds cost context but does not enhance parameter meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool lists recent API requests for the current key owner, using specific verbs and resource. It distinguishes itself from sibling tools like account_balance and account_daily_usage by focusing on request history.

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 cost-related guidance (low credits, free cached results) but does not explicitly state when to use this tool versus alternatives like account_daily_usage or account_most_used_routes. Usage context is implied but not formalized.

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

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

No annotations exist, so the description carries the full burden. It discloses credit cost, caching (free if cached), and failure policy (never charged), adding useful behavioral context. Missing details on auth, rate limits, or data refresh beyond cache.

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: first clearly states purpose, second adds key behavioral details. No wasted words, 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?

The description covers purpose and cost/caching, but lacks details about return structure (no output schema), common errors, or example use cases. For a data-fetching tool, more completeness would be beneficial.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description does not add new parameter-level meaning beyond the schema, meeting the baseline of 3.

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

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

The description clearly states it retrieves 'Amazon seller storefront metadata and product listings', which is a specific verb+resource pair. Among the many sibling tools focused on other platforms, this tool is uniquely Amazon and distinct in its scope.

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

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

Provides practical usage context: cost (~89 credits), caching behavior, billing policy, and a warning about cross-platform URLs. However, it lacks explicit when-to-use or when-not-to-use alternatives, though the sibling list implies it's the only Amazon seller 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?

Discloses credit cost, caching behavior, and no charge on failures, which adds value given no annotations. But does not explicitly state read-only nature or any required authentication.

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, front-loaded with purpose and key behavioral notes. No unnecessary words.

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

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

For a 2-parameter tool without output schema, the description is adequate but lacks detail on what engagement metrics are returned. Could be more complete.

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

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

Schema covers 100% of parameters with detailed descriptions. The tool description adds no extra parameter meaning, so baseline of 3 is appropriate.

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

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

The description clearly states the tool retrieves metadata and engagement for a Bluesky post, distinguishing it from profile or user posts tools. However, it could be more specific about what 'metadata' includes.

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 on when to use this tool versus alternatives like bluesky_profile or bluesky_user_posts. Mentions cost but no exclusions or prerequisites.

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

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

Discloses cost (~1 credit), caching behavior (free for cached results), and no charge for failures. Without annotations, this provides useful operational transparency, but lacks detail on returned data fields or mutation indicators.

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, front-loaded purpose, zero wasted words. Efficiently communicates core info.

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

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

For a simple tool with no output schema, the description covers purpose and key behavioral traits. Could mention what specific stats are returned, but overall sufficient for 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?

Schema coverage is 100%, so baseline is 3. The description adds cost context but does not enhance meaning of parameters beyond what the schema already provides (url format, cache 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 the tool retrieves profile info and stats for a Bluesky account, with a specific resource (profile) and implied verb (get). It distinguishes from sibling tools like bluesky_post_details and bluesky_user_posts which focus on specific posts or user posts.

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 on when to use this tool vs alternatives or any prerequisites. The description omits context about filtering or comparison with similar profile tools for other platforms.

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

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

No annotations exist, but the description adds cost and caching behavior ('~3 credits; cached results are free, failures are never charged'). However, it lacks details on post ordering, pagination, or auth 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?

Single sentence with no superfluous words. Cost and caching info are efficiently included. Could be improved by briefly mentioning output format.

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

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

For a simple tool with 3 parameters and no output schema, the description is adequate but incomplete. It does not describe what fields are returned (text, images, timestamps), which would help an agent.

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

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

Schema coverage is 100%, so the schema already documents all parameters. The description adds minimal extra meaning ('Recent posts'), which is not crucial beyond schema 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?

Description clearly states 'Recent posts from a Bluesky profile' with a specific verb ('get' implied) and resource (Bluesky profile). It distinguishes from sibling tools that target 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 tool name and description make the platform-specific use clear. While no explicit when-to-use or alternatives are given, the context of sibling tools (e.g., instagram_channel_posts, twitter_user_tweets) implies this is for Bluesky posts.

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 useful behavioral context: costs ~17 credits, cached results are free, failures are not charged, and cache can be controlled via the 'cache' parameter. Missing details on rate limits, authentication, or what constitutes a failure.

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, front-loading the purpose and key behaviors in a single sentence. The URL parameter description also includes important guidance. No fluff, but could be slightly more structured.

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

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

Despite lacking an output schema, the description fails to explain what data the tool returns (e.g., ad content, metrics, targeting details). This gap forces the agent to guess, reducing completeness for a moderate-complexity 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 covers both parameters (url and cache) with descriptions. The description adds context: cost implications of caching ('cached results are free') and the critical rule about cross-platform URLs, which goes beyond the schema's formatting hint.

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 'Meta/Facebook ad details', which clearly identifies the tool's purpose. It is specific to Facebook ads and distinguishes from similar tools for other platforms via the tool name, but does not elaborate on what 'details' entails.

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 guidance via the URL parameter, warning against cross-platform URLs. It also mentions caching and cost behavior. However, it does not explicitly state when to use this tool over sibling tools like facebook_ad_library_search or facebook_ad_library_company_ads.

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

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

The description discloses cost (~17 credits), caching behavior (cached results free, failures never charged), adding value beyond absent annotations. However, it does not detail error handling or the exact output 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?

Two sentences: the first concisely states the purpose, the second adds cost and caching details. 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?

The description covers purpose, parameters, cost, and caching. It is mostly complete given the tool's simplicity, though it lacks details on the output format or error states.

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

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

With 100% schema description coverage, the schema already documents both parameters. The tool description adds the context that output is transcript text, but this does not significantly enhance parameter understanding.

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

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

The description clearly states the action ('Extract creative text'), the resource ('Meta/Facebook ad'), and the output ('transcript text'). It distinguishes from sibling tools like 'facebook_ad_library_ad_details' which likely returns 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 includes a warning in the 'url' parameter about not using cross-platform URLs, which provides a constraint. However, it does not explicitly guide when to use this tool versus alternatives like 'facebook_ad_library_ad_details' or 'facebook_transcript'.

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

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

With no annotations, the description discloses credit costs, caching, and failure billing. However, it omits behaviors like rate limits, output structure, or error handling, leaving 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?

Two-sentence description, front-loaded with purpose and efficient with cost information. No unnecessary words.

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

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

For a 4-parameter tool with no output schema, the description covers input, cost, and caching but lacks output details or typical usage context, leaving some uncertainty 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?

All parameters are fully described in the schema (100% coverage). The description adds cost/caching context but does not enhance understanding of individual parameters beyond schema.

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

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

The description clearly states it retrieves ads for a Facebook page or Meta Ad Library URL. It distinguishes itself from sibling tools like facebook_ad_library_search or facebook_ad_library_ad_details by specifying the input type.

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 explicit guidance on when to use this tool versus alternatives. The description does not provide context for selection among siblings such as facebook_ad_library_search_companies.

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 credit costs (~70 credits), caching behavior (cached results free, failures never charged), and that fresh data is default. This covers key behavioral traits but omits rate limits or permissions.

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

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

Two concise sentences, front-loaded with purpose and cost. Every sentence adds value: first defines action and cost, second clarifies caching and billing. 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?

With no output schema, the description does not explain return format or pagination. However, given the tool's simplicity (search by keyword) and sibling tools covering details, the input and behavioral info is adequate. Slight gap in output specification.

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

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

Schema description coverage is 100%, so all parameters are documented. The description adds value by mentioning cost and caching but does not enhance parameter meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Search Meta/Facebook ads by keyword.' It uses a specific verb (search) and resource (Meta/Facebook ads), and distinguishes it from sibling tools like facebook_ad_library_ad_details or facebook_ad_library_company_ads.

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 on when to use (keyword search) and mentions cost and caching behavior but does not explicitly guide on when to use over siblings like facebook_ad_library_search_companies. It is clear but lacks explicit exclusion logic.

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?

Transparently discloses cost (~70 credits), caching behavior (free cached results, fresh by default), and failure policy (never charged). Since no annotations exist, description fully handles this dimension.

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

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

One sentence conveying core info succinctly with no redundant words. 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?

Lacks details about the output format (e.g., list of advertisers, fields returned) and pagination. For a search tool, this information would help an agent understand what to expect.

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

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

Schema has 100% description coverage and already documents each parameter well. Description adds no further semantics beyond the schema, meeting baseline but not exceeding.

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 ('Find') and the resource ('advertisers/pages in the Meta Ad Library by name'), distinguishing it from sibling tools like 'facebook_ad_library_search' or 'facebook_ad_library_ad_details'.

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 key usage context about credit costs, caching, and failure policy, but does not explicitly compare with alternatives or state when not to use this tool over 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?

The description discloses key behavioral traits beyond what annotations provide: credit cost (~30 credits), caching behavior (cached results free, default false), and failure policy (never charged). These are not in annotations (none provided). However, it does not mention rate limits, authentication requirements, or return structure.

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

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

The description is two sentences with no wasted words. It front-loads the purpose and adds critical cost/caching information. 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?

For a 4-parameter tool with no output schema, the description provides cost and caching info but omits return format, error handling details, or rate limits. While minimally viable, it could better equip the agent to handle responses.

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 covers all 4 parameters with descriptions (100% coverage). The description adds no additional semantics beyond the cost context. The schema descriptions are sufficiently clear, so the baseline score of 3 is appropriate.

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

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

The description clearly states 'Replies to a specific Facebook comment' with a specific verb ('Replies') and resource ('Facebook comment'). This distinguishes it from sibling tools like facebook_comments (which gets top-level comments) and other platform comment reply tools. The additional cost mention further clarifies its 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?

No explicit guidance on when to use this tool versus alternatives like facebook_comments or other comment-related tools. The input schema includes a note about not passing cross-platform URLs, but this is a constraint, not usage direction. There is no when-not-to-use or mention of prerequisites.

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

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

No annotations are provided, so the description bears full responsibility. It discloses cost and caching behavior but omits critical behavioral traits such as authentication requirements, rate limits, error handling, or the nature of the operation (read-only vs. destructive). This is insufficient for an informed 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 a single, efficient sentence followed by a brief bullet-like note on cost and caching. Every word adds value, with no redundancy. It is appropriately concise and front-loaded.

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

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

Given the lack of an output schema and annotations, the description is too sparse. It fails to explain return format, pagination, error responses, or any prerequisites. For a data retrieval tool with three parameters, this leaves significant gaps for an AI 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 100% description coverage, but the description adds value by explaining the URL platform constraint, caching semantics, and limit bounds (default 50, max 500), which go beyond the schema's minimal 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 states 'Comments on a Facebook post,' clearly indicating the tool retrieves comments. While it distinguishes from many sibling tools by platform and type, it does not explicitly differentiate from the closely related facebook_comment_replies tool, which is for replies to comments.

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 cost and caching details and warns about using correct URL platform, offering some usage context. However, it does not specify when to choose this tool over alternatives like facebook_comment_replies or other comment-related tools, leaving the agent to infer usage.

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

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

With no annotations provided, the description carries the full burden. It discloses caching and failure behavior, but does not mention rate limits, response format, or what happens with invalid/malformed URLs (beyond the schema's cross-platform warning). Adequate but not comprehensive.

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

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

The description is concise (under 25 words) and front-loaded with the core purpose. It wastes no words, though it could benefit from a slightly more structured format including intended output.

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

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

There is no output schema, and the description does not specify what 'details' are returned (e.g., likes, comments, timestamp). For a data retrieval tool, this lack of return value context reduces completeness significantly.

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

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

Schema description coverage is 100%, so the baseline is 3. The top-level description does not add additional meaning to the parameters beyond what the schema already provides (e.g., URL platform constraint, cache behavior).

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 'Details' and the resource 'Facebook video or post'. While it does not explicitly differentiate from sibling tools like 'facebook_page_details' or 'facebook_profile_posts', the focus on video/post is specific enough to convey its purpose.

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

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

The description provides useful context on cost (1 credit), caching (free if cached), and failure policy (never charged). However, it offers no guidance on when to use this tool versus alternatives, nor explicit prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It discloses credit cost, caching policy (cached results free), and failure charging (never charged), adding valuable transparency beyond the schema.

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

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

Two sentences, no wasted words. Purpose is front-loaded, and each clause provides distinct useful 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 retrieval tool with 2 parameters, the description covers the output fields (date, location, attendees, tickets) and additional cost/caching context. Lacks output format details but remains 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 100% with descriptions for both parameters. The description adds value by explaining the caching behavior in relation to the cache parameter and emphasizing the URL platform constraint.

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 details for a Facebook event, listing specific data fields (date, location, attendees, tickets). This distinguishes it from sibling tools like facebook_event_search (which searches) and facebook_details (which is more general).

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 retrieving details of a specific Facebook event via URL and mentions caching and cost behavior, but it does not explicitly state when to use this tool versus alternatives or provide 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?

Without annotations, the description carries the full burden of behavioral disclosure. It explicitly states cost (~40 credits), free caching, and no charge for failures. This is valuable context beyond basic functionality. However, it does not describe what the tool returns (e.g., list of events with fields), which would improve transparency.

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

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

The description is compact with two sentences: one for purpose and one for cost and caching. Every sentence is meaningful, no redundancy, and it is front-loaded with the primary 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?

Given no annotations and no output schema, the description should cover return values. It does not explain what information the search results contain (e.g., event name, date, location). The cost detail is helpful, but the lack of output format guidance leaves the agent uncertain about 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?

Schema description coverage is 100%, so the baseline is 3. The description does not add new information about parameters beyond what the schema already provides (e.g., min 2 chars, cache behavior, defaults). No extra semantic value is contributed.

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

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

The description clearly states the verb 'Search' and the resource 'Facebook events', specifying the method 'by topic and/or location'. This distinguishes it from sibling tools like facebook_event_details which are for retrieving details of a specific event. The description is unambiguous and effective.

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 such as facebook_event_details or facebook_marketplace_search. The description includes cost and caching behavior but fails to indicate use cases or exclusions.

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

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

Given no annotations, the description provides useful behavior like cost (~12 credits), caching policy, and failure charges. However, it lacks important details like read-only nature, authentication requirements, and response format.

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

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

The description is short and to the point, but the first sentence is fragmented and could be more active. It efficiently conveys cost/caching info.

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?

With no output schema, the description fails to indicate what the response contains (e.g., list of posts, fields). Also missing hints on sorting, filtering, or required permissions beyond 'public group'.

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

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

Schema coverage is 100% with clear param descriptions. The tool description adds no additional parameter meaning, so baseline 3 is appropriate.

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

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

The description clearly states the tool provides posts from a public Facebook group, distinguishing it from similar tools like facebook_profile_posts. The verb is implied but the resource is 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?

No guidance on when to use this tool versus alternatives (e.g., facebook_profile_posts, facebook_page_details). The only implicit constraint is the URL platform matching, but no explicit usage 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?

The description adds useful cost context (1 credit, free cache, no charge on failure). With no annotations, this adds value but does not cover other behavioral aspects like rate limits or return structure.

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

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

Extremely concise single sentence that front-loads the purpose. Every word adds value with no fluff.

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

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

Given simple parameters and good schema coverage, the description is fairly complete. It covers cost and caching, but is silent on the output structure or exact details returned. Adequate for the tool's simplicity.

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

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

Schema coverage is 100%, baseline 3. Description adds extra guidance for the url parameter (URL platform must match, no cross-platform) beyond the schema. The cache parameter description aligns with schema.

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

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

The description clearly states it retrieves details for a single Facebook Marketplace listing. This distinguishes it from siblings like facebook_marketplace_search which searches for listings.

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

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

The description implies use for a single listing given a URL, but does not explicitly state when to use this tool versus alternatives like search tools. No exclusions or when-not-to-use guidance.

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

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

No annotations are provided, so the description carries full burden. It discloses cost (~17 credits), caching behavior (free cached results, failure not charged), and default cache=false. However, it lacks details on rate limits, side effects, or output format.

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

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

The description is two sentences: first states purpose, second adds cost/caching info. No filler, front-loaded with essential 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 should hint at return format (e.g., place IDs, coordinates) but does not. Cost and caching are covered, but output and usage context are missing. Adequate but incomplete for an agent to fully understand 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?

Schema description coverage is 100% (all 3 params have descriptions). The tool description adds no extra parameter semantics beyond the schema, so baseline 3 is appropriate.

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

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

The description states 'Find Facebook Marketplace location candidates for a city or place query,' which is a specific verb (find) + resource (location candidates) + input (city/place query). It clearly distinguishes from sibling tools like facebook_marketplace_search, which filters listings.

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

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

The description does not provide guidance on when to use this tool versus alternatives (e.g., facebook_marketplace_search). It mentions cost and caching but no situational advice or preconditions.

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 behavioral disclosure burden. It mentions credit cost, caching, and failure billing, which are useful. However, it omits other behavioral traits such as authentication requirements, rate limits, or whether it is a read-only operation.

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

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

The description is two sentences long, front-loading the purpose and adding cost/cache details. Every sentence is useful; there is no redundancy or 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?

The description covers cost and caching but lacks information about output format, pagination, or example queries. Given the five parameters and no output schema, it is minimally adequate but not comprehensive.

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

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

Schema coverage is 100%, and the description does not add meaningful information beyond the schema. The description includes a cost note but no per-parameter elaboration. Baseline 3 is appropriate as schema already documents parameters.

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

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

The description clearly states the verb 'Search' and resource 'Facebook Marketplace listings by keyword and location.' It conveys the core function but does not explicitly differentiate from sibling tools like facebook_marketplace_item or facebook_marketplace_location_search, though the name implies general search.

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 cost and caching information but offers no guidance on when to use this tool versus its siblings. There is no mention of prerequisites, scenarios, or when not to use it.

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

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

No annotations are provided, so the description carries full responsibility. It discloses cost (~1 credit), caching behavior (free if cached), and failure policy (not charged). However, it does not mention rate limits, data freshness, or whether this is a read-only operation.

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

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

The description is a single sentence, concise and front-loaded with purpose. It efficiently conveys key points (cost, caching) without extraneous words. Could be slightly more informative but remains effective.

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 no output schema, the description lacks details on what specific stats are returned (e.g., likes, followers). It also does not describe the output format or any pagination. Given the low complexity, more context is needed to make it complete.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema; the URL and cache parameters are already well-documented in the schema. No additional parameter semantics are provided in the description.

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

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

The description clearly states 'Info & stats for a Facebook page,' indicating a specific verb (retrieve) and resource. However, it does not differentiate from sibling tools like 'facebook_details' which may have overlapping functionality.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives. Usage is implied by the tool name and purpose, but no when-not or alternative recommendations 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 cost (~40 credits), caching policy (cached results free), and failure charging (never charged). With no annotations, it provides valuable behavioral traits. However, it omits details on auth requirements, rate limits, output format, and error handling beyond charging.

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, front-loaded with purpose, then cost info. Every sentence adds value. No redundancy or filler.

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, cost, and caching, but lacks output format details, pagination info, error handling behaviors, and differentiation from similar tools. For a tool with no output schema, more context on return data would be helpful.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds no extra meaning for parameters beyond what's in the schema. The cost note is unrelated to 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?

The description clearly states 'Events from a Facebook profile or page,' specifying the resource (events) and source (Facebook profile/page). It distinguishes from sibling tools like facebook_event_details (specific event) and facebook_event_search (search events), but lacks an explicit verb like 'List' or 'Get.'

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 on when to use this tool versus alternatives like facebook_event_search or facebook_event_details. The cost and caching info provides some context for selection, but no explicit when-to-use or when-not-to-use criteria.

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 useful behavioral information: cost (~12 credits), free caching for 24h, and no charge for failures. However, it does not disclose the return format, pagination behavior, authentication requirements, or what happens with private profiles. Annotations are absent, so 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 very concise: a single sentence plus cost info. It is front-loaded and wastes no words. However, it could be slightly more structured or include key behavioral notes without becoming verbose.

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?

With no output schema and no annotations, the description should explain the return format, data structure, and pagination. It only mentions retrieving photos, leaving agents to guess the response shape. Given the complexity and many sibling tools, this is insufficient.

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

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

Schema coverage is 100% with well-described parameters. The outer description adds cost context but no additional parameter-level meaning. The schema descriptions already provide adequate guidance for url, cache, and limit (including default values and constraints). Baseline of 3 is appropriate.

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

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

The description clearly states the tool retrieves photos from a Facebook profile or page. It is specific about the resource (photos) and the platform (Facebook), but does not distinguish from sibling tools like 'facebook_profile_posts' or 'facebook_page_details' that may also involve photos.

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 cost and caching details but gives no guidance on when to use this tool versus other Facebook profile tools such as 'facebook_profile_posts' or 'facebook_page_details'. The url parameter warns against cross-platform URLs, but that is a constraint, not usage guidance.

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

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

With no annotations, the description discloses credit cost and caching policy, which adds useful behavioral context. However, it does not cover failure behavior, pagination, 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?

Two sentences, no wasted words, front-loaded with purpose. Efficient and well-structured.

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

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

Adequate for a simple retrieval tool with 3 parameters. Covers purpose and cost, but lacks details on response format, error handling, or ordering. No output schema, so more context on return values would help.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add extra meaning to parameters beyond the schema's own 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?

Description clearly states it retrieves the latest posts from a Facebook profile/page, using specific verb and resource. It distinguishes from siblings like facebook_group_posts and facebook_profile_events.

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 explicit guidance on when to use this tool vs alternatives. The schema description for url adds a platform-matching note, but the main description lacks usage context or exclusions.

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

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

With no annotations, the description carries the transparency burden. It discloses cost (~36 credits), caching behavior (free for cached results, failures never charged), which is useful. However, it does not mention rate limits, authentication needs, pagination, or what constitutes a failure, leaving 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: two short sentences that front-load the purpose and then add cost/caching details. Every sentence adds value with no redundancy. Ideal length 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?

For a simple list-retrieval tool with 3 parameters and no output schema, the description covers purpose and cost but lacks details on response format, pagination, ordering (what 'latest' means), and error scenarios. It is minimally adequate but not thorough.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter information beyond what is already in the input schema; the schema descriptions are clear and complete. The description does not augment or clarify parameters further.

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 'Latest Reels from a Facebook profile/page' clearly states the tool's purpose: retrieving reels (a specific content type) from a Facebook profile or page. This implicitly distinguishes it from sibling tools like facebook_profile_posts or facebook_profile_photos, though it does not explicitly name them.

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

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

The description provides no guidance on when to use this tool versus alternatives. The only usage-related information is in the input schema (URL platform constraint), which is not part of the description. No when-not or alternative tools are mentioned.

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, caching, and failure behavior. No annotations provided, so description covers key behavioral traits. Lacks mention of rate limits or auth, but still informative.

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

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

Two sentences: purpose first, then usage details. No wasted words. Front-loaded with essential 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?

Covers purpose, usage, and parameters adequately for a simple tool. No output schema, but return format is implied. Could mention what the summary includes, but not essential.

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

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

Schema coverage is 100%, and description adds context: url emphasizes public and platform matching, cache explains behavior and default. Adds value beyond schema.

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

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

Clearly states it provides an AI summary of a Facebook video or post. The platform-specific naming distinguishes it from siblings like tiktok_summarize.

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 cost info (credits), caching behavior, and failure charging. Parameter description warns against cross-platform URLs. Could be more explicit about when to use vs other Facebook 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 mentions credit cost, caching, and failure charging, which are useful behavioral traits. However, it does not disclose details like language support, transcript format, or limitations, leaving some transparency 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—two sentences that front-load the purpose. Every sentence adds value: the first states the function, the second covers cost and caching. No wasted words.

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

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

Despite having no output schema or annotations, the description does not describe the return format, error behavior, or any edge cases. It covers only basic cost and caching. For a transcript tool, more context (e.g., format, language) is expected to be sufficiently complete.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no new parameter semantics beyond what the schema provides (e.g., the URL cross-platform warning is already in the schema description). Baseline 3 is appropriate.

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

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

The description clearly states 'Transcript of a Facebook video,' which identifies the verb (get transcript) and resource (Facebook video). It distinguishes from sibling tools like tiktok_transcript or youtube_transcript 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?

The description explains cost and caching policies ('Costs ~2 credits; cached results are free, failures are never charged') and the URL parameter description explicitly warns against cross-platform URLs, providing clear context for 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?

With no annotations provided, the description adds valuable behavioral context: it discloses cost (~12 credits), caching behavior (cached results free, fresh fetches non-free), and failure policy (free). This goes beyond the schema and helps the agent anticipate usage implications.

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. The first sentence states the purpose; the second covers cost, caching, and failure policy. Every sentence adds value with no redundancy or unnecessary detail.

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

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

While the description covers cost and caching well, it omits any mention of the output format (e.g., a list of events) and does not explain how the tool handles edge cases (e.g., private activity). Given the absence of an output schema, this lack of output context reduces completeness.

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

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

Schema description coverage is 100%, so each parameter (cache, limit, username) is already documented in the schema. The description does not add new meaning beyond what the schema provides, earning the baseline score of 3.

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

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

The description clearly states the tool retrieves 'Recent public activity for a GitHub user,' providing a specific verb and resource. However, it does not explicitly differentiate from sibling tools like github_contributions or github_user, leaving some ambiguity.

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 caching and cost behavior, telling users when to use cache ('Set true to serve from the 24h response cache') and that failures are free. But it provides no guidance on when to choose this tool over alternatives (e.g., github_user for profile info), limiting decision support.

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 credit cost (~3 credits), caching policy (24h cache, free results), and failure charging (never charged). However, it lacks details on what constitutes 'recent' contributions, the exact data returned (e.g., count, types, time range), and any rate limits, which are important 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 two sentences (32 words), each serving a distinct purpose: the first states the tool's primary function, the second provides key operational details (cost, caching, failure policy). No extraneous information. Front-loaded with the most essential information.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description covers purpose, cost, and caching. However, it does not describe the return format or what 'summary' entails (e.g., total contributions, breakdown by repository or type), and 'recent' is ambiguous. An agent might need additional context to interpret results 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?

Schema coverage is 100%, baseline 3. The description adds valuable context for the 'cache' parameter by explaining the 24h cache duration and default behavior (fresh fetch). The 'username' parameter description ('GitHub username or profile URL') is clear but already present in the schema. Overall, the description adds marginal but useful extra 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?

The description clearly states 'Summary of recent public GitHub contributions', using a specific verb ('summarizes') and resource ('public GitHub contributions'). This distinguishes it from sibling tools like github_activity, github_followers, etc., which cover different aspects of GitHub 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 provides context on credit cost, caching behavior, and failure charging, which helps decide when to use cache for free results. However, it does not explicitly state when to use this tool over alternatives like github_activity or github_repositories, leaving the agent without clear usage boundaries.

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?

Adds useful behavioral info: cost (~12 credits), caching (free if cached), no charge on failures. However, lacks details on error handling, rate limits, or response structure.

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

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

Two short sentences, no fluff. Efficiently communicates purpose, cost, and caching behavior.

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; description does not detail return format or pagination. For a list tool, missing info on response structure and how to handle multiple pages.

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

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

Schema covers all 3 parameters (100% coverage). Description adds marginal value by reinforcing billing and caching context but does not provide new 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 'List GitHub followers' with a specific verb and resource. It distinguishes from sibling tools like github_following by focusing on followers, though it does not explicitly contrast them.

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

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

No guidance on when to use this tool versus alternatives. Mentions caching and billing but not context for choosing github_followers over github_following or github_user.

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 significant behavioral context beyond annotations by disclosing costs (~12 credits), caching behavior (free for cached results), and failure charging (never charged). This compensates for the lack of annotations.

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

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

The description is extremely concise with two sentences, front-loading the purpose. Every word adds value without redundancy.

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

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

For a simple list tool without an output schema, the description covers purpose, parameters, pricing, and caching adequately. It could mention the return type for completeness, but overall it's 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 100% with descriptions for all three parameters. The description adds minimal extra meaning beyond the schema, such as brief notes on cache and limit behavior, but does not substantially enhance understanding.

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

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

The description states 'List accounts a GitHub user follows' with a specific verb and resource, clearly distinguishing it from sibling 'github_followers' which lists followers instead.

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 pricing and caching behavior but does not provide explicit guidance on when to use this tool versus alternatives like 'github_followers' or 'github_user'. Usage context is implied rather than explicit.

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

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

The description discloses cost (12 credits), caching behavior (free cached, failures not charged), and billing per result (via limit). With no annotations, this is valuable behavioral info. It does not cover pagination or rate limits, but the cost 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?

Two concise sentences: first states purpose, second adds key operational details. No fluff, front-loaded with essential info. 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?

The tool has 4 well-documented parameters. No output schema exists, so missing return format is a gap. However, for a straightforward list tool with clear parameter descriptions, the description is adequate for selection and invocation. Siblings confirm it's the only pull-request listing tool.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds cost context related to cache and limit but does not enhance the meaning of individual parameters beyond their schema descriptions. The value added is marginal.

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 'List repository pull requests' with a specific verb and resource. It distinguishes from sibling tools like github_repositories and github_activity, which cover different GitHub entities.

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

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

The description implicitly differentiates this from other GitHub tools by naming pull requests. However, it lacks explicit guidance on when to use alternatives or when not to use this tool. The cost and caching hints provide some usage context, but no direct comparisons.

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

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

Discloses cost (~12 credits), caching behavior (free cached results), and failure policy (never charged). With no annotations, the description provides helpful operational info but omits authentication requirements, rate limits, and whether only public repos are listed.

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

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

Two sentences with zero waste. First sentence states purpose, second adds cost/caching detail. Nearly perfect conciseness and front-loading.

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?

Adequate for a simple list tool but lacks details on output format (no output schema) and scope (e.g., public vs. private repos). Given no output schema, description could mention response structure. Contextual completeness is acceptable but not outstanding.

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

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

Schema coverage is 100% with clear descriptions for all three parameters. The description adds no additional parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description clearly states 'List a GitHub user's repositories' with a specific verb and resource. It implicitly distinguishes from sibling tools like 'github_repository' (singular) and 'github_trending_repositories' by focusing on a user's own repos, but could explicitly mention scope (public vs private).

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 on when to use this tool versus alternatives like github_repository or github_trending_repositories. Does not specify prerequisites (e.g., valid username) or context such as listing only owned repos vs. contributed repos.

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, but the description compensates by disclosing important behavioral traits: cost of ~3 credits, caching semantics (free when cached), and that failures are not charged. This gives the agent useful operational knowledge beyond the input schema.

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

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

Two short sentences: first states purpose, second provides cost/caching behavior. No fluff, front-loaded with essential information, and every sentence adds value.

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

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

For a single-repo details tool with no output schema, the description covers the key behaviors (cost, caching, failure policy) and hints at return content (stars, forks, metadata). It is sufficient for an agent to select and invoke correctly, though it could elaborate on what 'metadata' includes.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add extra meaning for parameters; it only mentions caching behavior globally. No examples or constraints are provided for the 'repo' or 'cache' parameters beyond what the schema already offers.

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 'Repository details, stars, forks and metadata' which precisely defines the tool's function. The singular form distinguishes it from sibling tools like github_repositories (plural) and other GitHub-specific tools (e.g., github_activity, github_contributions).

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

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

Provides clear context on cost (~3 credits) and caching behavior ('cached results are free, failures are never charged'), guiding when to use the cache parameter. However, it does not explicitly mention when to use this tool over siblings like github_repositories for listing repos or github_user for user details.

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 valuable behavioral context beyond the input schema, including cost (~12 credits), caching policy (free if cached), and failure policy (never charged). This compensates for the lack of annotations.

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

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

The description is extremely concise with two front-loaded sentences. Every word contributes value—purpose, cost, and caching behavior—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?

While the description covers cost and caching, it lacks any mention of the return format or typical response structure. Since there is no output schema, the description should hint at what the user receives (e.g., list of developer profiles).

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

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

Schema coverage for parameters is 100%, so the baseline is 3. The description does not add additional meaning beyond what the schema already provides for 'q', 'cache', and 'limit'.

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 'Search popular GitHub developers,' which uses a specific verb and resource. It distinguishes itself from the sibling tool 'github_trending_repositories' by focusing on developers rather than repositories.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'github_user' or 'github_trending_repositories.' It lacks any when-to-use or when-not-to-use 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 discloses cost (~12 credits), caching behavior (free cached results), and failure handling (not charged). This adds significant behavioral context beyond the schema, though it could mention pagination or sorting order.

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

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

The description is two sentences, front-loading the purpose first, then adding cost and caching details. Every sentence adds value, with no redundancy 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 no output schema, the description covers purpose, cost, caching, and billing. It does not specify the return format (e.g., list of repositories with details), but the name and context imply the output structure. A bit more detail on return values would improve completeness.

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

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

Schema coverage is 100%, so the description does not need to add much. It provides high-level context ('by stars or query') but does not enhance understanding of specific parameters beyond what the schema already states (e.g., descriptions of q, cache, limit are clear in the schema).

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

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

The description specifies 'search trending repositories by stars or query', clearly stating the verb, resource, and scope. It distinguishes from siblings like github_repositories which likely have broader search capabilities.

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 trending repositories but does not explicitly state when to use this tool over alternatives like github_repositories or github_repository. No exclusion criteria or 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?

Discloses cost (~3 credits), caching behavior (free when cached, failures not charged), which is valuable beyond the schema. No annotations exist, so this carries the burden well, though it misses details like data freshness 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?

Two short sentences front-load the purpose and add essential cost/caching info. No filler; every word 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?

While cost and caching are covered, the description omits return format or fields (no output schema). For a simple tool this might be acceptable, but it could be improved by noting what 'profile details' includes.

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

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

Schema coverage is 100%, so parameters are already documented. The description adds no new semantic meaning to parameters beyond what's in the schema; the cost note relates to overall behavior, not parameter 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 it provides 'Public GitHub user profile details', which is a specific verb-resource pair. It distinguishes from sibling tools like github_followers or github_repositories that cover specific sub-parts of a GitHub user.

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

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

No guidance on when to use this tool versus alternatives such as github_followers, github_repositories, or other profile-related tools. The description only mentions cost and caching, not use-case distinctions.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It reveals credit cost, caching behavior, and that failures are not charged. However, it lacks information on error handling (e.g., what happens with invalid URLs), rate limits, or whether the operation is purely read-only. More detail would improve transparency.

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

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

The description is extremely concise—two sentences with no unnecessary words. It front-loads the purpose and follows with key behavioral details (credits, caching). Every sentence adds value, and no repetition of schema fields occurs.

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 3 parameters and no output schema, the description should compensate by hinting at the return structure. It does not explain what 'ad details' includes (e.g., fields, format). Additionally, it omits common context like error responses or pagination. The description is too minimal for a data retrieval tool of this complexity.

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

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

Schema description coverage is 100%—all three parameters have descriptions in the input schema. The tool description adds no additional meaning beyond what the schema already provides. It does not elaborate on the format of 'creative_id' or clarify the 'cache' and 'country' parameters further. Baseline score of 3 is appropriate since the schema handles semantics adequately.

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 'Google ad details by Transparency Center URL', clearly identifying the resource and input method. However, it does not specify what kind of details are returned (e.g., ad creative, performance metrics), which slightly reduces specificity. The name already distinguishes it from sibling 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?

The description provides important usage context about credit costs and caching: 'Costs ~17 credits; cached results are free, failures are never charged.' This helps the agent decide when to use cache. However, it does not mention when to use this tool versus alternative Google Ad Library tools like advertiser search or company ads, nor does it specify prerequisites such as URL format requirements.

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

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

The description discloses cost (~45 credits) and caching behavior (cached results free, failures never charged), which is useful. However, it does not mention whether the tool is read-only, any authentication needs, rate limits, or what the response contains.

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, each serving a purpose. First sentence states the action, second provides cost and caching details. 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, the description should explain what the tool returns (e.g., list of advertisers with details). It only mentions cost and caching, missing the overall outcome. Among many sibling ad library tools, it doesn't clarify the result format.

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

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

The input schema has 100% description coverage, so the schema already documents all parameters. The description adds no extra meaning beyond the schema.

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

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

The description states 'Search Google Ads advertisers' which is a clear verb+resource. It distinguishes from sibling tools like 'google_ad_library_ad_details' and 'google_ad_library_company_ads' by focusing on searching for advertisers rather than details or company-specific ads.

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 on when to use this tool versus alternatives like Facebook Ad Library tools or other Google Ad Library tools. No mention of prerequisites or when not to use it.

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

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

No annotations provided, so description carries full burden. It discloses credit cost (~67 credits), caching behavior (free if cached, never fail charge), indirectly indicating safety. For a read-only data retrieval tool, this is good 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 very concise: one sentence for purpose and a short note on costs. Information is front-loaded. However, it could be slightly more structured (e.g., separate cost and caching details).

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

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

Given 4 parameters and no output schema or annotations, the description covers purpose, cost, and caching. It lacks information on return format, pagination, or what constitutes an 'ad.' This is adequate but not fully complete for a complex tool.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds no new parameter meanings beyond the schema; it reiterates cache behavior but does not enhance understanding of limit, country, or advertiser format. Baseline 3 is appropriate.

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

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

The description clearly states 'Google Ads Transparency Center ads for an advertiser,' specifying the verb (retrieve) and resource (ads for an advertiser). It distinguishes from siblings like facebook_ad_library_company_ads and google_ad_library_ad_details by platform and scope.

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

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

The description mentions caching and credit cost but does not provide explicit guidance on when to use this tool vs alternatives like google_ad_library_ad_details or google_ad_library_advertiser_search. No 'when not to use' advice is given.

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

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

With no annotations provided, the description effectively discloses caching behavior (24h cache, free), credit cost, and failure policy. It implies a read-only operation but does not explicitly state it. Overall, it provides sufficient behavioral context for safe usage.

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 long, each serving a distinct purpose: defining the tool's action and specifying cost and caching details. No redundant information; every word 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?

Despite having no output schema, the description labels the return as 'Full public Instagram profile,' which sets appropriate expectations. Given the tool's simplicity (2 params, no nested objects), the description is sufficiently complete. Some users might want a list of fields, but it's 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?

Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying that userId accepts profile URLs, @handles, or usernames beyond the numeric ID. The cache parameter behavior is also reiterated, adding clarity beyond the schema's description.

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

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

The description explicitly states it retrieves a full public Instagram profile, specifies accepted identifier types (numeric ID, URL, @handle), and mentions cost and caching behavior. It clearly distinguishes the tool's purpose from siblings by focusing on 'basic profile' 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 provides clear guidance on when to use caching (free, 24h) and cost implications (1 credit, no charge on failures). However, it does not explicitly differentiate from sibling tools like instagram_details or instagram_channel_details for profile retrieval, leaving some ambiguity.

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 mentions cost (~1 credit), caching behavior (free if cached), and failure policy (never charged). These add useful behavioral context beyond the schema, but it omits other important details such as rate limits, data freshness beyond the cache, or read-only 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 covering purpose, cost, and caching. Every word earns its place 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?

Without an output schema, the description should detail what data is returned. It only says 'profile info & stats' without specifying fields (e.g., followers, posts count). This is insufficient for an agent to understand the output fully.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds a note about cross-platform URL restrictions and cache behavior, but does not significantly enhance parameter understanding beyond what the schema already provides.

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

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

The description clearly identifies the tool as providing profile info and stats for an Instagram account. It is specific about the resource (Instagram account) and the action (retrieving info/stats), though it does not explicitly differentiate from similar tools like instagram_details.

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 lacks guidance on when to use this tool versus alternatives. Among many Instagram-related siblings, no usage context or comparison is provided, leaving the agent to infer applicability.

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

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

With no annotations, the description discloses credit cost, caching behavior (free for cached results), and failure charging policy. This adds useful behavioral context beyond the schema, though it omits details like 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 two sentences, no wasted words. The core purpose is front-loaded, and additional info (cost, caching, charging) is presented efficiently.

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

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

Given no output schema and four parameters, the description covers purpose, cost, caching, and error handling. It lacks details on returned fields and rate limits, but for a straightforward fetch tool, it is reasonably complete.

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

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

Schema coverage is 100%, so the schema already documents all parameters. The description adds minimal extra meaning, mentioning cost per result for the limit parameter and cache default behavior. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves 'Latest posts from an Instagram profile,' with a specific verb and resource. Among sibling tools like instagram_channel_details and instagram_channel_reels, it uniquely identifies posts, making selection unambiguous.

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

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

The description implies usage for fetching Instagram posts but does not explicitly state when to use this tool versus alternatives such as instagram_tagged_posts or instagram_profile_search. No when-not or alternative 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?

With no annotations, the description provides valuable behavioral details: cost (~6 credits), caching policy (free cached results, never charged for failures), and implication of read-only operation. It does not cover auth or rate limits, but these are reasonable omissions for a list tool.

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

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

The description is a single sentence that efficiently front-loads the purpose, then adds cost and cache details. Every piece of information is valuable and there is no redundancy.

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

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

Given the tool has 4 parameters, no output schema, and no annotations, the description covers purpose, cost, caching, and platform matching. It does not explain pagination or return format, but the schema details cursor sufficiently. Overall, it provides adequate context for a list tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful context beyond schema: billing per result (for limit), default cache behavior (false), and platform-matching requirement for URL. This enhances parameter understanding.

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

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

The description clearly states 'Latest Reels from an Instagram profile', specifying the verb (list/retrieve), resource (reels), and scope (profile). It distinguishes from sibling tools like instagram_reels_search, instagram_reels_by_audio_id, and instagram_trending_reels, which have different purposes.

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 lacks explicit guidance on when to use this tool versus alternatives. Given many sibling tools for Instagram reels (e.g., search, trending, by audio), the description should advise on use cases, but it only provides cost and caching details.

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

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

With no annotations, the description discloses cost behavior and caching policy but omits other behavioral details like rate limits, authentication needs, or whether replies are included. The description adds value beyond schema by explaining charging, but it is 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?

Two concise sentences, front-loaded with purpose. No filler or redundant information. Every sentence is informative and 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?

The description lacks information about return values, format, or authentication, which is critical for a comment tool with no output schema. The schema covers parameters, but behavioral context like response structure is missing, making it 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 100%, so baseline is 3. The description adds value by noting that cached results are free (context for cache parameter) and that limit billing is per result (not in schema). This enhances understanding of parameter implications.

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 comments on Instagram posts or reels, using specific verb 'Comments' and platform 'Instagram'. It distinguishes from siblings by specifying the platform, as opposed to Facebook, TikTok, or YouTube comment 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 cost and caching guidance ('Costs ~45 credits; cached results are free') but lacks explicit when-to-use or when-not-to-use instructions. No alternatives or prerequisites are mentioned, though the cost info helps in decision-making.

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 cost behavior and caching info, which is helpful. However, it lacks details on what data is returned, error handling, or any prerequisites.

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 with two sentences. The first sentence delivers the purpose, the second adds critical cost and caching context. No extraneous 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 2-param tool with no output schema, the description covers basic purpose and cost. But it lacks any info about response format or additional behavioral expectations, so it's adequate but not complete.

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

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

Schema coverage is 100% and parameter descriptions in the schema are already detailed. The tool description itself adds no additional param information, so baseline 3 is appropriate.

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

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

The description clearly states 'Details for an Instagram post or reel', specifying both the action and resource. It implicitly distinguishes from sibling tools focused on profiles, search, or other aspects of Instagram.

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

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

No guidance on when to use this tool vs alternatives like instagram_basic_profile or instagram_channel_details. It only mentions cost and caching, which are not about selection criteria.

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

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

The description discloses credit cost, caching behavior, and that failures aren't charged, providing important behavioral context beyond the schema.

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

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

Two sentences with no wasted words, front-loading 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?

Missing output schema; description doesn't specify the return format (e.g., HTML snippet), which an agent might need to know how to use the embed.

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 covers both parameters fully; description doesn't add new parameter details but provides useful context on credits and caching.

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 generates embed HTML for Instagram posts, reels, or profiles, distinguishing it from sibling tools that fetch data or details.

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

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

While it doesn't explicitly list when to use vs. alternatives, the purpose is specific enough that an agent can infer its use case. Lacks explicit exclusions or comparisons.

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

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

With no annotations, the description carries the burden. It discloses billing behavior (cost, caching, failure policy) but does not cover other aspects like rate limits, authentication, or data freshness beyond the cache parameter. No contradiction with missing annotations.

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

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

Two sentences, each serving a distinct purpose: purpose and billing behavior. No fluff or redundancy. Efficient and clear.

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

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

Given the lack of output schema and simple parameters, the description covers purpose and cost but misses details like default limit (20), result type, and guidance on cache usage. Adequate but not fully complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal semantics beyond the schema (e.g., 'Hashtag without the #' is already in schema). No significant extra value.

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

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

The description clearly states 'Search Instagram posts by hashtag', specifying the verb, resource, and parameter. It distinguishes this tool from siblings like instagram_profile_search or instagram_tagged_posts by focusing on hashtag-based search.

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 searching posts by hashtag but does not explicitly tell when to use this tool over alternatives or provide exclusions. No mention of other Instagram search 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 burden. It explicitly mentions cost (~1 credit), caching (free for cached), and failure policy (never charged), which are useful behavioral traits. However, it omits details like auth requirements 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 extremely concise—one sentence plus a short cost note. No wasted words, front-loaded with purpose, and well-structured.

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

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

The tool is simple (2 params, no output schema). The description covers purpose, cost, and caching behavior adequately. It could mention the expected response structure, but given the simplicity, 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?

Schema coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema's own descriptions for 'q' and 'cache'. The cost mention is a bonus but not parameter-specific.

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 resolves an Instagram handle to a public profile, using specific verbs ('resolve') and resource ('Instagram account name or @handle'), distinguishing it from siblings like instagram_basic_profile or instagram_details.

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 resolving handles but does not explicitly state when to use this tool versus alternatives like instagram_basic_profile or instagram_details. No exclusions or alternative guidance 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?

The description discloses cost (~28 credits), caching benefits (cached results free), and charging policy (failures never charged). Since no annotations were provided, these behavioral traits add value beyond the structured schema.

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

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

Extremely concise: one sentence stating purpose plus a cost/cache note. No superfluous words. Front-loaded with the core function.

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, yet the description does not describe return values or pagination. However, for a straightforward list tool with well-documented parameters, missing output info is a minor gap. The cost and caching notes add useful 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?

Schema description coverage is 100%, so the baseline is 3. The description does not add new meaning to parameters beyond what the schema provides. The cost note relates indirectly to the cache parameter but does not enhance semantic understanding.

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

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

The description clearly states the tool retrieves posts/reels using an Instagram audio ID. The verb 'using' and resource 'Instagram audio ID' make the action specific, and it distinguishes from related Instagram tools like 'instagram_reels_search' and 'instagram_trending_reels'.

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 explicit guidance on when to use this tool versus alternatives. The description implies its use for audio-based retrieval but does not mention when not to use it or provide references to sibling tools. Guidance is limited to cost and caching notes.

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 provides important behavioral details: cost (~12 credits), caching policy (free if cached), and failure charging (never charged). This adds transparency beyond the schema, though rate limits or authentication requirements are not mentioned.

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 with a brief note on costs—front-loaded, no extraneous words, perfectly concise for the information provided.

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, yet the description omits any mention of return value structure (e.g., what fields are returned, pagination). This leaves the agent uncertain about the result format, making it incomplete for a search tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds overarching context about costs and caching but does not enhance individual parameter semantics beyond the schema's existing 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?

Description clearly specifies verb 'Search', resource 'Instagram Reels', and method 'by hashtag/keyword'. This distinctively differentiates from sibling tools like instagram_hashtag_search (general hashtag search) and instagram_trending_reels (trending reels).

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 explicit guidance on when to use this tool versus alternatives like instagram_hashtag_search or instagram_trending_reels. Cost and caching info is provided but does not address selection 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 adds value by disclosing cost (~4 credits), caching behavior (free cached results, failures not charged). However, it does not mention authentication needs, rate limits, or return format, which would be helpful for an AI agent.

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

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

The description is concise, consisting of two short sentences that front-load the purpose and then add key behavioral facts. No unnecessary words, though could be slightly more structured (e.g., bulleted pricing 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 tool has 3 parameters and no output schema, the description covers the core purpose and some behavioral aspects, but omits expected output structure and error behavior. It is adequate but not fully comprehensive for an agent to invoke without ambiguity.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add any parameter-specific meaning; it only describes the overall function. Since the schema already provides full parameter documentation, no further detail 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 'AI summary of an Instagram Reel,' specifying the verb (summarize) and resource (Instagram Reel). This distinguishes it from sibling summarization tools for other platforms like TikTok and YouTube.

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

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

The description implicitly indicates usage for Instagram Reels, and the tool name reinforces this. It does not explicitly exclude other platforms, but the context of sibling tools makes the scope clear. No explicit when-not or alternative names, but clarity is high.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It mentions credit cost, caching policy, and failure handling, which adds value beyond the schema. However, it omits details like data freshness, pagination, 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?

Two sentences with zero wasted words. The main purpose is front-loaded, followed by key cost and caching info. Highly efficient.

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

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

For a simple retrieval tool with 3 parameters and no output schema, the description covers purpose and caching but lacks clarity on output format, error handling, and best practices for cache usage. Adequate but not comprehensive.

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

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

Schema coverage is 100% with clear descriptions for all three parameters. The description does not add new parameter-level details beyond what the schema already provides, meeting the baseline for high 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 the tool retrieves 'Posts an Instagram user is tagged in,' a specific verb and resource. Although it doesn't explicitly differentiate from sibling tools like instagram_channel_posts, the purpose is unambiguous and informative.

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 on when to use this tool over other Instagram post retrieval tools. Missing context about prerequisites, use cases, or alternatives. The cost and caching info are operational, not comparative.

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?

Description discloses cost (2 credits), cache behavior, and failure policy. However, with no annotations provided, it should also mention potential side effects or auth requirements. The information is useful 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?

Extremely concise: two short sentences. First sentence states core purpose, second adds important behavioral notes. 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?

Covers purpose and cost/error behavior. Lacks description of output format (e.g., plain text with timestamps), which would help the agent anticipate results. Adequate but not complete.

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

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

Schema coverage is 100% with detailed descriptions for all 3 parameters. The tool description adds no additional meaning beyond the schema, earning baseline score.

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

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

The description clearly states the tool provides a transcript of an Instagram Reel, using specific verb and resource. It distinguishes from sibling transcript tools for other platforms and content types.

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

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

No explicit guidance on when to use this tool versus alternatives. The description only provides pricing and caching info, not selection criteria.

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 burden. It mentions cost, caching, and failure policy, which are positive. However, it does not describe what the tool returns (list of reels with metadata? just IDs?), nor does it explain behavioral traits like pagination, rate limits, or data freshness beyond cache. The lack of output schema further increases the need for behavioral 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?

The description is extremely concise: two sentences that convey purpose, cost, caching policy, and failure handling. No wasted words, and the key information is front-loaded. 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 has 3 parameters, no output schema, and no annotations, the description is adequate for basic usage, covering purpose and cost/caching. However, it lacks details about return format, pagination, and differentiation between 'trending' and 'explore' (are they the same?), leaving some gaps. Sibling tools are many, so more context would help.

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

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

Schema coverage is 100% with clear descriptions for cache, limit, and country parameters. The description adds minimal extra meaning beyond the schema (e.g., 'by country' aligns with the country parameter; 'cached results are free' relates to cache). Baseline 3 is appropriate as the schema already does the heavy lifting.

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

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

Description clearly states the tool fetches trending Instagram Reels/Explore posts by country. The verb 'Trending' and resource 'Instagram Reels / Explore posts' are specific and unambiguous. The tool name also reinforces this purpose, and it is distinct from sibling tools like instagram_reels_search or instagram_channel_reels.

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 cost and caching guidelines ('Costs ~28 credits; cached results are free, failures are never charged'), which helps the agent decide when to use it. However, it does not explicitly state when to use this tool versus alternatives (e.g., instagram_reels_search for search vs. trending), nor does it provide exclusions or prerequisites.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses credit cost, caching behavior (free for cached results), and that failures are not charged. It does not mention rate limits, auth needs, or specific metadata fields, but still provides useful operational context beyond the schema.

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

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

The description consists of two concise sentences, with the primary purpose stated first and operational details (cost, caching) in the second. No unnecessary words or redundancy.

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

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

The description lacks information about the output structure or return format, which is not covered by an output schema. For a metadata retrieval tool, knowing the returned fields would be beneficial for the agent. However, the input-side details are well covered by the 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?

Schema coverage is 100%, so the schema already documents all parameters. The description adds context about caching costs but does not provide additional parameter-level detail beyond what the schema offers.

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 clip metadata from a specific URL or recent channel clips, using a specific verb (Kick) and resource (clip metadata). The platform-specific name and description differentiate it from sibling tools like twitch_clip.

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

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

The description does not explicitly provide when-to-use/not-use guidance or mention alternatives. However, the tool's focus on Kick clips implies usage context, but lacks explicit exclusions or comparisons to similar tools.

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

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

No annotations are provided, so the description carries the full burden. It adds valuable operational details: cost (~4 credits), caching behavior (free if cached), and charging policy (failures not charged). This is useful disclosure, though it could mention 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 extremely concise: two sentences covering purpose, cost, and caching. No unnecessary words or redundancy. Ideal length.

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

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

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description is mostly complete. It explains what the tool does and its cost model, but lacks details on the exact profile metadata returned.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the 'url' parameter with a cross-platform warning but adds no new semantic 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 it provides 'Public Komi page links and profile metadata,' indicating a specific verb and resource. While the name distinguishes it from sibling platform tools, the description itself does not explicitly differentiate it from siblings like 'instagram_basic_profile' or 'facebook_page_details', relying on the tool name for platform identity.

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 explicit guidance on when to use this tool versus alternatives is given. Usage context is implied by the tool name and focus on the Komi platform, but there is no mention of exclusions or preferred scenarios.

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

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

With no annotations, the description transparently discloses credit cost (~17 credits), cached results being free, and failure not charging. This adds useful behavioral context beyond the schema. However, it omits details like rate limits or data freshness beyond caching.

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 front-loaded sentences: the first states purpose, the second adds cost details. 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 no output schema, the description could better outline what 'metadata and engagement' includes. While acceptable for a simple tool, it lacks detail on return structure or data fields, leaving some ambiguity.

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

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

Schema description coverage is 100%, so parameters are well-documented. The description adds the credit cost context but does not significantly enhance parameter understanding. Baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves Kwai/Kuaishou post metadata and engagement, with a specific verb-resource pair. The required URL parameter reinforces the platform specificity, distinguishing it from sibling 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 does not explicitly state when to use this tool versus alternatives. However, the parameter description advises against cross-platform URLs, implying a platform-specific use case. Lack of explicit guidance on scenarios or when not to use it lowers the score.

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