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

No annotations are present, so the description carries full burden. It discloses the tool is free ('0 credits'), defaults to last 7 days, and describes the output structure in detail. No destructive behavior is mentioned, which is appropriate for a read-only usage tool. The description adds significant 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?

The description is two sentences, highly efficient. The first sentence states purpose and output. The second covers cost, defaults, and parameter usage. Every sentence provides 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?

No output schema is provided, but the description fully explains the return format (records array with fields). It covers all essential aspects: purpose, cost, parameter customization, and output structure. For a simple usage tool with well-documented parameters, this is 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 descriptions for both parameters. The description adds meaning beyond the schema by specifying they are 'Unix-ms strings' and explaining default behavior (defaults to last 7 days). This helps the agent understand format and 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's purpose: 'Get your CreatorDB API usage.' It specifies the output format (records with date, requestCount, etc.) and distinguishes itself from sibling tools focused on specific platform data or searches. The verb 'get' and resource 'API usage' are unambiguous.

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

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

The description provides clear context: 'Free to call (0 credits). Defaults to last 7 days; pass start/end as Unix-ms strings to widen the window.' It gives default behavior and how to modify it. It does not explicitly state when not to use this tool but implies it for checking usage, not data retrieval.

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 fully covers behavioral traits: it explains that when data is missing the endpoint returns a placeholder shape (all 0.0), specifies how to interpret missing data (male+female=0 means missing, not no gender), and notes the credit cost. This is thorough beyond expectations.

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

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

The description is concise and well-structured: a single paragraph that starts with the main purpose, then details each returned field with types and examples, and ends with special behavior and cost. Every sentence contributes value without redundancy.

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

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

Given no output schema, the description adequately explains all return values, including field types, example values, and how missing data is represented. The parameter is simple and fully documented. No important context is missing.

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

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

The single parameter 'uniqueId' is already well-described in the input schema (handle, no '@' prefix, no URL). The description repeats this information but adds no new semantic meaning beyond what the schema provides. Schema coverage is 100%, 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 identifies the tool's purpose: retrieving an Instagram creator's audience demographics. It lists specific fields (audienceLocations, audienceGender, audienceAvgAge, audienceAgeBreakdown) and distinguishes from sibling tools like get_instagram_profile or get_instagram_performance by focusing on demographic 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 implies usage for obtaining Instagram audience demographics and mentions a cost of 10 credits. However, it does not explicitly state when not to use this tool or suggest alternatives among the many sibling tools. No prerequisites or conditions 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 (15 credits) and scope of emails, but no annotations are provided. Missing information on authentication requirements, rate limits, or behavior when no emails are available.

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, clear sentence with essential information. 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?

Adequate for a simple tool with one parameter and no output schema. Covers purpose, scope, and cost, but lacks details on errors or empty results.

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

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

Input schema already describes the parameter well (100% coverage). Description adds no new meaning beyond what the schema provides.

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

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

Clearly states it retrieves contact email addresses for Instagram creators, specifying the type (business/public-listed). Distinguished from siblings like get_instagram_audience and get_instagram_profile.

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?

Implied usage by tool name and description, but no explicit guidance on when to use vs. alternatives or prerequisites. The cost mention provides some context but not comparative usage.

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

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

No annotations are provided, so the description carries full responsibility. It discloses key behavioral traits: views only for reels, exclusion of content from the last 4 days from metric calculations, exclusion of old pinned posts, and the credit cost. This goes beyond basic functionality.

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

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

The description is concise (two sentences) yet packs essential information without superfluous words. It is front-loaded with the main purpose and then specifies nuances and constraints efficiently.

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

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

For a single-parameter tool with no output schema, the description is complete. It explains what content is retrieved, what metrics are included, exclusions, and cost. An agent has sufficient context to decide if this tool meets the need.

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 for the single parameter 'uniqueId', with clear context (no '@' prefix, no URL). The description adds no new details beyond the schema, so the baseline of 3 is appropriate.

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

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

The description clearly states that the tool retrieves recent Instagram content (images and reels) with per-item engagement details. It lists specific fields returned, and the resource and verb are clearly identified. It distinguishes from sibling tools that focus on other aspects like audience or profile.

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 contextual details like credit cost and date exclusions, but does not explicitly state when to use this tool versus alternatives like search_instagram_content. There is no guidance on prerequisites or when not to use it, leaving usage 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?

No annotations are provided, so the description carries the full burden. It details the metrics returned (avg/median/min/max likes, comments, views; engagement formula; consistencyScore with bands and requirement; ranking; growth trends; content count). It also notes the credit cost (2 credits) and the limitation that IG has no all-time window.

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 front-loaded with the main purpose and then systematically details the data blocks. Every sentence adds value with no redundancy. It is appropriately sized given the complexity of the returned data.

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

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

Despite no output schema, the description thoroughly explains what the tool returns, including the important metrics, consistency score bands, and growth trends. It also mentions the credit cost, making it complete for an agent to understand the tool's functionality.

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

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

The only parameter, uniqueId, is already described in the input schema (handle format, no '@' or URL). The tool description does not add further semantic meaning beyond this, but schema coverage is 100%, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves engagement metrics for an Instagram creator's first-page content. It specifies the returned data blocks (imagesPerformanceRecent, reelsPerformanceRecent, engagement, ranking, growth trends, posting cadence) and distinguishes from siblings by noting IG has no all-time window (YouTube-only).

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 purpose is clear, but there is no explicit guidance on when to use this tool versus alternatives like get_instagram_performance_history. However, the mention of 'first-page content' and 'IG has no all-time window' implies appropriate use cases.

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 reveals that the tool returns a 'histories' array of timestamped snapshots and costs 3 credits. This provides insight into output structure and resource usage. No annotations exist, so the description carries full burden and does so well, though it could mention error handling or 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?

The description is two sentences, each with distinct, essential information: first sentence states purpose and metrics, second sentence describes output and cost. No wasted words. Front-loaded with the core 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 two simple parameters, no output schema, and no annotations, the description covers all critical aspects: what the tool does, what it returns (histories array), key metrics, time range, and cost. No major gaps for an agent to use it correctly.

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

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

The input schema provides complete descriptions for both parameters (uniqueId and pastDayRange), covering 100% of parameters. The description adds no new information about parameters beyond what the schema already states, only confirming the time range concept.

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 specific verb and resource: 'Get daily metric snapshots' and lists exact metrics (followers, content count, first-page engagement). It clearly distinguishes from sibling 'get_instagram_performance' by emphasizing the historical, snapshot-based nature and time range.

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 suggests use for historical daily data but does not explicitly mention when to use this tool versus alternatives like 'get_instagram_performance' or other sibling tools. No exclusion or context for when not to use 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?

The description discloses the cost (2 credits) and implies a read operation via 'Get', but lacks explicit information about safety, authentication requirements, or error behavior. Without annotations, more transparency is needed.

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 highly concise: two sentences that efficiently convey purpose, returned fields, alternative tool, and cost. No unnecessary text.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers all essential aspects: what is returned, cost, and related tool. It is complete for the complexity level.

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

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

The schema already fully describes the single parameter with 100% coverage. The description does not add any additional meaning beyond what is in the schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves an Instagram creator's profile with a specific list of fields. It distinguishes itself from sibling tools like list_instagram_niches and get_instagram_audience by its focus on 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 explicit context by mentioning list_instagram_niches as an alternative for niche taxonomy browsing. However, it does not specify when to use this tool versus other profile-related siblings like get_instagram_audience, 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?

With no annotations, description fully discloses behavioral traits: costs 5 credits, scans limited posts, only detects indexed brands, and that empty sponsorList is not proof of no sponsors. This helps set expectations.

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

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

Concise at 3 sentences plus a bullet point, front-loaded with purpose, then return type, then caveats. Every sentence provides necessary information without redundancy.

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

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

Although there is no output schema, the description thoroughly explains the return structure (sponsorList fields, engagement shape) and critical context (credit cost, limitations), making it complete for selection and invocation.

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

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

Schema description coverage is 100% and the description adds value beyond schema by specifying format restrictions (no '@' prefix, no URL) and providing an example ('natgeo'), which clarifies 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?

Description clearly states the tool gets sponsored content grouped by brand, using specific verb 'Get' and resource 'creators sponsored content'. It distinguishes from siblings like get_sponsor_creators and get_sponsor_performance by focusing on brand-level grouping.

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 implicit guidelines by explaining limitations (scans only ~20-30 posts, only indexed brands) and cost (5 credits), but does not explicitly mention when to use alternative tools among the many sibling sponsorship tools.

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

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

With no annotations, description fully discloses important behavior: IG block is always null (backend limitation), the expensive nature (25 credits), and the fixed age breakdown buckets. This enables informed invocation decisions.

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

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

Description is concise, covering all necessary details in a few sentences. Front-loaded with purpose and output structure. Slight redundancy in enumerating age buckets could be trimmed, but otherwise 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?

Despite no output schema, description thoroughly documents return structure: two optional blocks (data.youtube, data.instagram) each containing audienceLocations, audienceGender, audienceAvgAge, audienceAgeBreakdown (with explicit bucket list). Also covers cost and platform omission. All essential for agent to use 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% with parameter descriptions. Description adds value by explaining how to omit platform to get both datasets and clarifying the enum values (though schema already lists them). The cost warning is an extra behavioral cue.

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 returns aggregated audience demographics across all sponsored creators, distinguishing it from per-platform audience tools like get_youtube_audience. It specifies output fields and current limitation (IG always null), making purpose unmistakable.

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

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

Provides clear usage hint: 'Omit platform to request both.' Warns about high cost (25 credits). Does not explicitly exclude use cases or name alternatives, but the context of aggregated vs per-creator audience is implicit given sibling tools.

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

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

With no annotations, the description bears the full burden. It discloses the cost, aggregate nature of engagement rate, platform-specific windows, and pagination parameters. It omits read-only status (implied) and potential rate limits, but the coverage is good for a list endpoint.

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

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

The description is a single, well-packed paragraph that front-loads the purpose, includes key result fields, and ends with cost. Every sentence adds necessary information without fluff.

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

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

Given the tool has 6 parameters and no output schema, the description explains most critical aspects: purpose, returned fields, platform nuance, and cost. It lacks explicit pagination metadata in the response but covers the essentials 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 value by explaining that avgRecentSponsoredEngagementRate is cross-brand aggregate with platform-dependent windows, supplementing the schema. The cost note also informs parameter use.

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

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

The description clearly states the tool lists creators a brand has sponsored and explicitly calls it the inverse of get_*_sponsorship, effectively distinguishing it from sibling tools. It also specifies returned fields, leaving no ambiguity about 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?

The description tells when to use it (list sponsored creators) and notes the cost (25 credits per page) and platform limitations (no TikTok). It indirectly guides against misuse by mentioning the inverse tool, but does not explicitly list when not to use or alternative tools for different queries.

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

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

No annotations are provided, so the description must carry the burden. It discloses a cost of 2 credits, which is valuable behavioral info. However, it does not mention whether the operation is read-only, idempotent, or any side effects, leaving gaps for the agent.

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

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

The description is a single sentence that effectively front-loads the purpose and lists all returned fields, plus a cost note. Every piece earns its place with no wasted words.

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

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

Given the simplicity (one parameter, no output schema), the description adequately lists all returned fields and cost. It lacks mention of error handling or edge cases (e.g., brand not found), but is otherwise complete for a straightforward retrieval 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% (brandId description provided). The tool description adds no additional parameter meaning beyond the schema, so baseline score of 3 applies.

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

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

The description clearly states 'Get a single brand's full profile' and enumerates specific fields like name, alias[], logo, etc. This verb+resource structure distinguishes it from siblings such as get_sponsor_summary (presumably less detail) and list_sponsors (multiple brands).

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 detailed brand profile retrieval but does not explicitly guide when to use this versus alternatives like get_sponsor_summary or search_sponsors. No when-not-to-use or alternative mention 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?

The description discloses the credit cost (25 credits per page) and platform-specific differences (YouTube-only for estimatedCost/estimatedCreatorCPM, null for Instagram). No annotations were provided, so the description carries the burden and does well, but could mention rate limits or whether modifications occur.

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, front-loads the purpose, and uses a clear structure: purpose, return fields, cost note. Each sentence adds value without unnecessary repetition.

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

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

Given the tool has no output schema and no annotations, the description provides a good overview of return structure and cost. However, it lacks details on pagination behavior (how to iterate) and error conditions, leaving minor gaps for a tool with 6 parameters.

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 have descriptions in the schema (100% coverage), so the description does not need to elaborate much. It adds context about the return structure which indirectly helps choose values for parameters like sortBy, but does not directly enhance parameter understanding 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 returns detailed per-content sponsorship performance for a brand, listing the return fields and noting the credit cost. It distinguishes itself from generic performance tools by focusing on sponsorship data per content.

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 brand sponsorship performance but does not explicitly state when to use this tool versus alternatives like get_sponsor_summary or get_sponsor_creators. No when-not-to-use 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?

With no annotations provided, the description carries full burden. It explicitly discloses that Instagram spend/CPM/CPE always return null and that the tool costs 25 credits, providing valuable behavioral traits beyond what annotations would typically cover.

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

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

The description is two sentences, front-loaded with purpose, but the second sentence is a long, dense list of nested fields, which could be more readable. It earns its place but is not optimally structured.

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

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

Given no output schema, the description thoroughly explains the return value structure. However, it omits error handling, prerequisites (e.g., brand existence), or rate limits beyond the single cost warning. Still, for a simple one-parameter tool, it is fairly complete.

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

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

Schema coverage is 100% for the single parameter, and the description adds no additional semantics for brandId beyond the schema's example. The detailed output structure indirectly helps, but per the guidelines, baseline is 3 when schema coverage is high.

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 'Cross-platform sponsorship rollup for a brand', which is a specific verb+resource. It distinguishes itself from sibling tools like get_sponsor_creators or get_sponsor_performance by emphasizing the rollup nature across platforms and providing detailed output structure.

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 notes that the tool is expensive (costs 25 credits), implying it should be used judiciously. However, it does not explicitly state when to use versus alternatives (e.g., use get_sponsor_creators for detailed creator lists), so it lacks exclusion guidance.

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

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

Discloses missing data placeholder shape (all 0.0) and interpretation rule (male+female=0 as missing). Mentions credi cost. No annotations provided, so description carries the burden; adequately covers key 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?

Concise, front-loaded with purpose. Every sentence adds value: purpose, field list, missing data handling, cost. 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?

Covers output fields, missing data behavior, and cost. No output schema exists, so description compensates well. Could optionally describe output format more, but sufficient for a simple retrieval 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?

Single parameter uniqueId is described in schema with 100% coverage. Description adds example and clarifies no '@' prefix and no URL, providing extra clarity beyond the schema.

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

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

Clearly states 'Get a TikTok creator's audience demographics' with specific fields listed. Distinguishes from sibling tools like get_instagram_audience via platform prefix.

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. Usage is implied through purpose, but no when-to-use or 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?

Discloses cost (15 credits) but lacks other behavioral traits such as authentication requirements, rate limits, or error responses. No annotations exist, so description carries the full burden but does not fully cover.

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

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

Single, focused sentence with no unnecessary text. Efficiently conveys purpose, data scope, and cost.

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

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

Provides essential information for a low-complexity tool: what it does, input format, and cost. Could mention return structure (e.g., an array of emails) but not critical for basic 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% with one parameter. Description adds value by specifying format (no '@' prefix, no URL) beyond the schema's example. Provides concrete usage guidance.

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

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

Clearly states the verb 'Get' and the resource 'a TikTok creator's contact email addresses (public-listed)'. Distinguishes from sibling tools like get_instagram_contact by specifying platform and data 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 vs alternatives. The description implies usage for TikTok contact retrieval, but does not mention when not to use or compare with other TikTok tools like get_tiktok_profile.

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

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

No annotations provided, so the description fully covers behavioral traits: exclusions (last 4 days excluded from metrics, pinned posts >90 days excluded), cost (2 credits), and special fields (audio metadata). This is thorough.

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

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

The description is well-structured, starting with the main purpose, then detailing returned fields, exclusions, and cost. Each sentence adds value, though it could be slightly more concise.

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

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

For a single-parameter tool with no output schema, the description fully explains what is returned, special cases, and cost. No gaps remain for effective use.

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

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

The single parameter uniqueId is described with formatting rules (no '@', no URL) beyond the schema's type/description. Schema coverage is 100%, but the description adds practical usage guidance.

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

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

The description clearly states the tool retrieves a TikTok creator's recent videos with per-item engagement metrics. The verb 'Get' and resource 'videos' are explicit. It distinguishes from siblings like get_tiktok_profile by focusing on content detail.

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 a use case (trending-sound analysis via audio metadata) but lacks explicit when-to-use or when-not-to-use guidance. It does not compare with siblings like search_tiktok_content.

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

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

The description thoroughly discloses behavioral traits: returns videosPerformanceRecent with detailed metrics, engagement block, consistencyScore requirements, ranking blocks, growth trends, posting cadence, and cost. No annotations exist, so the description carries full burden and meets 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 a single paragraph but well-structured with clear sections for different metric groups. It is informative without being overly verbose, though could be slightly more concise.

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

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

Given no output schema, the description fully explains return values, including engagement metrics, consistencyScore bands, percentiles, growth trends, and posting cadence. Also includes cost. Complete for a retrieval 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% for the single parameter uniqueId. The description adds minimal extra context beyond the schema (e.g., no '@' prefix), 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 it gets a TikTok creator's engagement metrics on recent videos, listing specific metrics. It distinguishes from sibling tools like get_tiktok_performance_history and get_tiktok_profile by focusing on engagement metrics.

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 TikTok performance data and notes TikTok's lack of an all-time window, but does not explicitly state when to use this tool vs alternatives or provide exclusion 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?

With no annotations, the description is the sole source of behavioral info. It notes the cost but does not disclose rate limits, authentication needs, or any side effects. The read-only nature is implied but not explicit.

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

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

Two sentences, no fluff. Front-loaded with purpose and key details like the return array and cost. Every sentence is informative.

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

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

Covers the basic purpose and return type but lacks details on snapshot structure (e.g., which metrics are in each object) despite being a history tool. No output schema, so description should elaborate more.

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 descriptions. The description adds minimal extra meaning (e.g., 'over the past N days' is redundant with schema). No new syntax or constraints 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 retrieves daily metric snapshots for a TikTok creator, naming specific metrics (followers, content count, recent engagement). However, it does not distinguish from sibling tools like get_tiktok_performance or get_tiktok_profile.

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. It mentions the cost (3 credits) but lacks context on prerequisites or exclusion 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 carries full burden. It mentions cost (2 credits) and lists returned fields, but does not disclose authentication requirements, rate limits, or behavior on missing profiles. This is a moderate gap given no annotation support.

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

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

Three concise, front-loaded sentences with no fluff. Every sentence adds value: purpose, returned fields, alternative tool, and cost.

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

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

Given no output schema and no annotations, the description sufficiently lists returned fields and provides cost. It lacks error handling or null field behavior, but overall is fairly complete for a straightforward profile retrieval 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 provides 100% coverage for the single parameter (uniqueId) with a clear description. The tool description adds no additional param info beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves a TikTok creator's profile and lists specific fields (display name, follower count, etc.). It distinguishes from sibling tool list_tiktok_niches by noting that tool is for browsing niche taxonomy.

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 this tool vs alternatives: directs to list_tiktok_niches for browsing niche taxonomy. It also mentions cost (2 credits). However, it does not explicitly state when NOT to use this tool or cover other sibling tools.

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

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

No annotations provided, so description carries full burden. Discloses data fields, placeholder shape for missing data, and cost. Lacks rate limits or error conditions but sufficient for core behavior.

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

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

Two sentences, front-loaded with main purpose, then details. No fluff, every sentence adds value.

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

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

No output schema, so description explains return fields adequately. Missing details on error handling or rate limits, but for a single-parameter tool it's fairly complete.

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

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

Schema coverage is 100%, but description adds meaning by explaining channelId must be UC form and what forms are not accepted, which is beyond schema 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?

Clearly states it gets a YouTube creator's audience demographics and lists specific fields. Distinguishes from siblings by being platform-specific and audience-focused.

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 context on cost and missing data behavior but does not specify when to use over alternatives like get_tiktok_audience or get_instagram_audience. Implicit context but no explicit guidance.

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

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

No annotations are provided, so the description must carry the burden. It discloses the credit cost and the specific input format requirement, but does not reveal whether the operation is read-only, any potential rate limits, or what happens if the channel has no contact info.

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 short sentences that efficiently convey the purpose, input restriction, and cost. No unnecessary words, and the 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?

Given no annotations and no output schema, the description provides basic purpose and input constraints but does not explain the output structure (e.g., what 'for business + public' means in practice). It is adequate for a simple tool but leaves some context gaps.

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

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

Schema coverage is 100% with one parameter fully described in the schema. The description adds value by clarifying that only the immutable UC form is accepted and explicitly listing rejected URL types (@handle, /c/vanity, /user/legacy), which is beyond the schema's generic description.

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

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

The description clearly states it retrieves contact email addresses for a YouTube creator, specifying both 'for business' and public emails. The verb 'Get' and resource 'YouTube creator's contact email addresses' are precise, and it distinguishes from other YouTube tools by focusing on contact info.

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

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

The description mentions credit cost and the input constraint (only UC channel ID accepted, not handles or vanity URLs), providing some guidance on when to use. However, it does not explicitly state when to use this tool versus alternatives or when not to use it.

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

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

With no annotations, the description fully handles disclosure. It reveals credit cost (3 credits), the 4-day exclusion from metric calculations, and lists return fields. No contradictions, but missing details like authentication 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 two sentences, each carrying essential information. No redundancy or filler; 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?

Given no output schema, the description adequately outlines return fields (views, likes, etc.) and mentions credit cost. Minor gap: does not specify if pagination applies or what 'recent' means in terms of time range.

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 the single parameter (channelId) fully with a clear description. The tool description adds no further parameter meaning beyond 'recent content' context. With 100% schema coverage, baseline score 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 a YouTube creator's recent content (videos and shorts) with per-item engagement metrics like views, likes, comments, length, etc. This distinguishes it from siblings like get_youtube_performance (aggregate) and search_youtube_content (cross-creator 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 explains what it returns but does not provide explicit guidance on when to use this tool versus alternatives. It mentions filtering options and metric exclusions but lacks direct comparison to sibling tools.

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

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

No annotations are provided, so the description carries full burden. It discloses the credit cost (2 credits) and the data structure. However, it does not mention rate limits, authentication requirements, or potential side effects. For a read-only tool, the cost disclosure is useful, but other behavioral aspects are missing.

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

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

The description is front-loaded with the main purpose and then details the return structure efficiently. It is comprehensive but not overly verbose, with each sentence providing distinct information. The use of parentheses for the consistency score bands and growth metrics adds clarity without clutter.

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 must explain the return values thoroughly. It covers all major components: four sibling objects, statistics, engagement block, ranking, growth trends, and posting cadence. This is sufficient for an agent to understand the tool's output and differentiate it from other performance-related tools.

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

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

Schema coverage is 100% with a single parameter (channelId) that has a description. The description reinforces the format requirement (UC… form) but does not add new semantic meaning beyond the schema. Baseline 3 is appropriate as schema already documents the parameter.

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

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

The description clearly states the tool's purpose: 'Get a YouTube creator's engagement metrics.' It details the specific return objects (videosPerformanceRecent, shortsPerformanceRecent, etc.) and metrics, distinguishing it from sibling tools like get_youtube_performance_history which likely focuses on historical data. The verb 'Get' and the resource 'engagement metrics' are specific and unambiguous.

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

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

The description lacks explicit guidance on when to use this tool versus alternatives. It mentions the credit cost but does not specify prerequisites, exclusion criteria, or compare with sibling tools (e.g., when to use get_youtube_performance_history instead). The usage context is implied but not clearly stated.

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

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

With no annotations, the description must disclose all behavioral traits. It states the cost (3 credits) and that it returns an array of timestamped snapshots. However, it does not mention side effects (none expected), rate limits, or authorization requirements, leaving gaps for a tool that credits cost.

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

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

The description is three concise sentences, front-loaded with purpose, and includes essential details (metrics returned, array structure, cost). No redundant information.

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

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

No output schema is provided, so the description should fully explain the return format. It mentions the 'histories' array but does not detail the structure of each snapshot (e.g., field names, types). Metrics are listed but not mapped to fields, leaving 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?

Both parameters are fully described in the schema (100% coverage). The description adds no new parameter meaning beyond the schema's detailed notes (e.g., channelId format restrictions). 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 daily metric snapshots for a YouTube creator, specifying subscribers, total content count, and all-time performance. It distinguishes from sibling tools like get_youtube_performance (likely current data) and get_youtube_audience (audience-specific) by focusing on historical time series.

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 historical performance data over N days but lacks explicit guidance on when to use over alternatives. No mention of prerequisites (e.g., valid channelId) or when not to use (e.g., if only current data needed).

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

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

No annotations are provided, so the description must disclose behavior fully. It mentions the cost of 2 credits, outlines what data is returned (including specific fields), and clarifies that certain fields are platform-specific. It does not mention side effects, prerequisites, or potential errors, but lacks any contradictions.

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 detailed but efficient given the complexity of the return data. It front-loads the main purpose and then lists fields concisely. The list is somewhat long but each item is necessary to convey the scope of the profile data. No unnecessary 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?

Since there is no output schema, the description fully explains what data is returned, including nested fields like subscriberGrowth and pricing blocks. It also references related tools for resolving IDs and notes credit cost. For a single-parameter tool, this is complete and self-contained.

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 parameter already has a description explaining the format. The tool description adds substantial context: it specifies the required UC… form, provides an example, and explicitly warns against using @handle, /c/vanity, or /user/legacy URLs, which is valuable beyond the schema.

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

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

The description explicitly states 'Get a YouTube creator's profile:' followed by a comprehensive list of returned fields. The verb ('get') and resource ('youtube profile') are clear, and the tool is distinct from sibling tools like get_youtube_audience which focus on different aspects.

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 the tool (to retrieve profile data) and instructs to use list_youtube_topics/list_youtube_niches for resolving topic/niche IDs. It also notes that pricing data is YouTube-only, implying not to use it for Instagram/TikTok. However, it does not explicitly contrast with siblings like get_youtube_sponsorship that may share some fields.

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

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

No annotations provided, but the description covers limitations (only recent posts, only detected brands), cost, and return shape. It lacks mention of authorization requirements.

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

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

The description is slightly long but all sentences add value. It is front-loaded with purpose, then details, then caveats, making it efficient despite 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 only 1 parameter, no output schema, and no annotations, the description covers all necessary aspects: input requirements, output structure, caveats, and cost. It is fully complete for correct tool usage.

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

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

The schema coverage is 100% and the description adds crucial meaning beyond the schema, specifying the exact format of channelId (UC… form) and what is not accepted.

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

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

The description clearly states the verb 'Get' and the resource 'YouTube creator's sponsored content grouped by brand'. It specifies the return structure and caveats, and the tool name distinguishes it from siblings by platform.

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

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

The description provides clear context on when to use (for YouTube sponsorship data) and includes caveats about scope and cost, but does not explicitly contrast with alternative tools like get_instagram_sponsorship.

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

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

No annotations are provided. The description discloses the credit cost (3 credits) and implies a read operation, but lacks details on authentication requirements, rate limits, or error behavior when subtitles are unavailable. It provides some value beyond annotations but is not fully transparent.

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

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

The description is two concise sentences: the first states the purpose, the second provides a critical usage constraint and cost. No unnecessary words, well front-loaded.

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

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

The tool has no output schema, but the description does not hint at the output format (e.g., SRT, VTT). It also omits error cases. However, it covers key usage constraints and cost, making it moderately complete for a simple tool.

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

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

Schema description coverage is 100%, with both parameters (videoId and language) fully described in the schema. The description adds no additional parameter meaning, so the baseline score of 3 is appropriate.

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

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

The description clearly states the action (download subtitle track), the resource (specific YouTube video), and distinguishes itself from sibling tools like get_youtube_subtitles_meta by specifying 'Download' vs 'Meta', and explicitly says 'pass videoId, not channelId' to avoid channel-level confusion.

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 that it is per-video with a videoId, and mentions a credit cost, but does not specify when to use this tool versus alternatives like get_youtube_subtitles_meta. No explicit when-not-to-use guidance is given.

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

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

No annotations are provided. The description discloses a cost of 1 credit but does not mention rate limits, authentication needs, or other behavioral traits. Adequate for a simple read 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?

Four sentences with no waste. Purpose is stated first, followed by constraints, usage hint, and cost. Well-structured and efficient.

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

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

Given one parameter, no output schema, and simple functionality, the description adequately explains what the tool returns (language codes, auto vs human). References sibling tool for download. No gaps for 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% for the single parameter videoId, which includes the YouTube video ID explanation. The description adds context about single video usage but does not enhance semantic detail beyond the schema.

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

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

The description clearly states the tool lists available subtitle tracks for a single YouTube video, including language codes and type (auto vs human). It differentiates from siblings by emphasizing per-video usage and relation to get_youtube_subtitles_download.

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

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

Explicitly instructs to use before get_youtube_subtitles_download and notes per-video, not per-channel. Does not state when not to use, but context is clear for a listing tool.

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

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

No annotations provided, so description carries full burden. It discloses no parameters, costs 1 credit, large response (~10K+ entries), and granular subcategory format. No contradictions.

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

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

Four sentences with dense information, no redundancy. Front-loaded with main purpose. Could be slightly more structured but still concise.

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

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

Given no output schema, no annotations, and zero parameters, the description fully explains the tool's behavior, cost, response size, and relationship to other tools. Complete for its simplicity.

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

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

Input schema has zero parameters; schema coverage is 100% trivially. Description explicitly states 'Takes no parameters', which adds confirmation but no extra meaning 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 'List the full Instagram NICHE taxonomy' with specific verb and resource. It distinguishes from siblings by noting per-platform taxonomies are not interchangeable and mentions no 'topics' taxonomy for 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?

Explicitly tells when to use (to see all niches) and when not (use get_instagram_profile for a specific creator's niches). Also warns about response size and clarifies Instagram vs YouTube taxonomy.

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 pagination, credit cost, and the output fields. Though no annotations exist, the description implies a read-only operation without explicitly stating so, which is a minor gap.

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

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

Two sentences cover purpose, output, alternative, and cost. No wasted words, front-loaded with key information.

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

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

Describes returned fields, pagination mechanics, and sorting options. Lacks details on field interpretation (e.g., sponsoringPlatforms format) but overall adequate for a list tool with well-documented parameters.

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 good parameter descriptions. The description does not add additional meaning beyond what the schema 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 the tool lists every brand in CreatorDB with specific fields, and distinguishes from search_sponsors for filtered browsing. The verb 'list' and resource 'sponsors' are precise.

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

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

Explicitly says to use search_sponsors when filtering is needed and that this tool is for browsing the full catalog. Also mentions credit cost per page, guiding efficient use.

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

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

With no annotations, the description fully discloses behavioral traits: large response (~10K+ entries), credits cost, no parameters, and per-platform taxonomy distinctions. Also clarifies what TikTok does not have, preventing misuse.

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 dense and informative but slightly long as a single paragraph. Every sentence earns its place, but a structured format (e.g., bullet points) could improve scannability. Still concise overall.

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

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

Given no output schema and no parameters, the description covers all necessary aspects: purpose, usage, behavioral traits, and differentiators. Fully complete for a list endpoint.

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

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

No parameters, so baseline 4. Description adds value by explaining the behavior and constraints, even though schema provides full coverage (no params).

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 lists the full TikTok niche taxonomy with channel counts, distinguishes from sibling tools like list_youtube_niches by specifying per-platform taxonomies, and clarifies TikTok does not have 'topics'.

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: to see a creator's niches use get_tiktok_profile, warns about large response size, and mentions it costs 1 credit. Does not explicitly list when not to use but given alternatives, it is sufficient.

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

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

No annotations provided, so description carries full burden. It discloses cost (1 credit) and no parameters, but does not specify response structure or pagination. However, for a simple list tool, this is adequate.

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?

Highly concise; every sentence earns its place. The purpose is front-loaded, with details and guidance following efficiently.

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

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

For a zero-parameter tool with no output schema, the description is complete. It covers what the tool does, what data it returns, when to use alternatives, and cost.

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

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

No parameters exist, so baseline is 4. The description adds value by specifying output includes channelCount and gives examples, which enriches understanding beyond the empty 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 lists the full YouTube NICHE taxonomy with 14,000+ entries and channelCount per niche. It distinguishes from platform-specific sibling tools (list_instagram_niches, list_tiktok_niches) and provides examples.

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

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

Explicitly tells when to use (list all niches) and when not (to see a specific creator's niches, use get_youtube_profile). It also warns that per-platform taxonomies are not interchangeable.

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 fully discloses behavior: lists ~470+ entries, includes channelCount, takes no parameters, costs 1 credit, and is YouTube-only. No annotations exist, so the description carries the full burden and meets 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 concise and informative, but could be slightly more structured. It front-loads the main action and details, with minimal waste.

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

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

Given the tool has no parameters and no output schema, the description provides complete context: what it returns, its limitations, and cost. No gaps remain.

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

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

No parameters exist; schema coverage is 100%. The description adds value by explicitly stating 'Takes no parameters' and explaining the output content (topics with channelCount), earning a baseline 4.

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

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

The description explicitly states it lists the full YouTube topic taxonomy with channelCount per topic, and distinguishes it from sibling tools like get_youtube_profile. It also notes that Instagram and TikTok lack a topic taxonomy, reinforcing its unique 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?

It clearly states when to use (to see full taxonomy) and when not to (for a specific creator's topics, use get_youtube_profile). It also mentions cost (1 credit) and platform specificity.

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. Mentions dynamic credit cost (1-10 credits based on tokens) and that response includes a 'platform' field. Could be more explicit about read-only nature, but implied as a search 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?

Five sentences covering purpose, behavior, output, usage guidelines, and credit cost. Efficiently front-loaded with no redundant information.

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

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

Given no output schema and one parameter, the description covers the essential aspects: input, output fields (creatorList, platform), dynamic credit cost, and fallback guidance. Could benefit from mentioning additional response fields, but sufficient for a simple tool.

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

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

Schema coverage is 100% for the single parameter 'description', which already has a good description. The tool description adds context about AI processing but doesn't significantly enhance parameter understanding 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 the tool searches for creators using natural language across YouTube, Instagram, and TikTok. It distinguishes itself from sibling tools like search_youtube/instagram/tiktok by emphasizing its natural language interface and platform detection.

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

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

Explicitly says 'Best for exploratory queries where you don't know exact filter names — fall back to search_youtube/instagram/tiktok when you need precise control over filters.' Provides clear when-to-use and 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?

With no annotations, the description fully covers behavioral traits: cost model (1 credit per 10 filters, max 10), hashtag indexing, and the response structure. No contradictions exist, and all important operational details are disclosed.

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 no superfluous sentences. It front-loads the core purpose and cost, then logically presents platform-specific constraints and response format. Every sentence conveys necessary 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?

Despite lacking an output schema, the description explicitly lists the response fields (creatorList, totalResults, hasNextPage, nextOffset). All critical aspects for invoking the tool are covered: pricing, pagination, field mappings, and search options. No gaps remain.

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

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

Despite 100% schema coverage, the description adds significant value beyond the schema: it explains platform-specific field differences (totalFollowers vs totalSubscribers, niches vs topics) and provides concrete examples for hashtag values. This context helps the agent avoid common errors.

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

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

The description clearly states the tool searches Instagram creators with structured filters. It distinguishes from sibling tools like search_tiktok and search_youtube by mentioning platform-specific fields (totalFollowers, niches, hashtags). The verb 'Search' and resource 'Instagram creators' are unambiguous.

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

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

The description provides explicit guidance on when to use totalFollowers instead of totalSubscribers, mentions that Instagram uses 'niches' not 'topics', and explains the '#' prefix for hashtags. It implies the tool is for finding Instagram creators, but does not explicitly state when not to use it or list alternatives.

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

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

In absence of annotations, description thoroughly discloses behavior: response structure, field details, missing fields (no views/lengthSec), credit cost (2 credits per page), and semantic nuances (publishTime integer as 'days ago').

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?

Well-structured single paragraph that front-loads purpose and differentiation. Packs substantial information but remains readable. Could be slightly more concise by breaking into sections.

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

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

No output schema, but description covers all necessary context: response fields, filter capabilities, pagination, credit cost, data limitations. Complete for a search tool with complex filtering.

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% but description adds significant extra meaning: lists filterable fields, explains sort options, clarifies publishTime semantic split, and notes credit cost. Goes well 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?

Explicitly states it searches Instagram content (images, reels, slideshows) across CreatorDB's index, and clearly distinguishes from search_instagram which searches creators. Provides specific verb and resource.

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?

Clearly differentiates from sibling tool search_instagram by stating it returns posts instead of creators. Implies when to use but lacks explicit 'when not to use' statements.

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

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

With no annotations, the description carries the full burden. It discloses the credit cost and data coverage, which are useful behavioral traits. However, it does not mention authentication requirements, rate limits, or behavior for empty results.

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 providing essential information: purpose and return fields, then cost and coverage. 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?

The description explains the return fields, cost, and coverage, which is sufficient for a search tool with a well-documented input schema. However, without an output schema, it could mention pagination behavior or error cases.

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 any parameter information beyond what the schema provides, so it neither improves nor detracts.

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 the CreatorDB brand index using structured filters' and specifies the return fields. This distinguishes it from sibling tools that retrieve individual records (get_*) or search other content (e.g., search_creators_nls).

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 notes the cost ('Costs 2 credits per page') and coverage limitations ('Sponsor data covers YouTube and Instagram only — TikTok is not indexed for brands'). This helps the agent understand when to use the tool, but it doesn't explicitly contrast with alternatives or state when not to use it.

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

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

Discloses cost, pagination with nextOffset, response shape (creatorList, totalResults, hasNextPage), and coarser sponsorship filtering. No annotations exist, so description carries full behavioral burden.

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 paragraph with dense, valuable info. Could be slightly better structured (e.g., bullet points) but is front-loaded with purpose and efficient in word count.

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 cost, filter count, pagination, response fields, and platform differences. No output schema, but response fields are listed. Missing potential details like data freshness, but complete enough 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%, but description adds meaningful guidance: 'Use totalFollowers (NOT totalSubscribers)' for count thresholds, 'Hashtag values need the "#" prefix.' These go 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?

The description clearly states 'Search TikTok creators using structured filters,' specifying the action and resource. It distinguishes from sibling tools by mentioning platform-specific fields like totalFollowers and niches vs topics, making it unique among search tools.

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

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

Provides explicit guidelines: credit cost per 10 filters, max 10 filters, and platform-specific instructions (use totalFollowers, hashtag '#'). Lacks a direct contrast with search_creators_nls but offers clear context.

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

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

Discloses response structure, missing fields (no contentType, no isSponsored), engagement formula, and brand-attribution limitations. Adds context beyond typical, though no annotations are present to rely on.

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?

Dense single paragraph with front-loaded purpose; all sentences add value. Could benefit from bullet points but length is justified by complexity.

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 essential aspects: what it returns, what it doesn't, filterable fields, sorting, cost. Lacks explicit pagination explanation but offset/pageSize are in 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?

Adds value beyond 100% schema coverage by listing filterable fields in natural language, clarifying request/response naming (diggs vs likes), and explaining engagement formula.

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 searches individual TikTok videos, distinguishes from sibling search_tiktok (creators), and specifies the resource as 'individual 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?

Explicitly contrasts with search_tiktok for creators, lists filterable fields, notes cost per page. Could add explicit when-not-to-use scenarios but is sufficient.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals credit cost, filter limits, platform-specific field usage (totalSubscribers vs totalFollowers), and pagination mechanics. It does not mention side effects (likely read-only) or authentication, but the behavioral traits are well covered for a search 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 four sentences, each dense with useful information: purpose, billing, platform-specific field guidance, and response pagination. No extraneous words. It is well front-loaded with the main purpose.

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

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

Given the complexity of the tool (5 parameters, no output schema), the description covers the essential points: what the tool does, how filtering works (including costs and field naming), and how to paginate. The output schema is absent, but the description lists the response fields and pagination mechanism. It does not cover error handling or edge cases, but it is adequate for typical usage.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value beyond the schema: it explains the credit cost relation to filters, emphasizes using totalSubscribers over totalFollowers, and provides context about YouTube-specific fields (topics, niches) and pagination response structure. This extra information helps the agent use parameters correctly.

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

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

The description clearly states the tool searches YouTube creators using structured filters. It distinguishes from sibling tools like search_instagram by highlighting YouTube-specific fields (topics, totalSubscribers vs totalFollowers) and explaining pagination. The verb 'search' and resource 'YouTube creators' are specific and unambiguous.

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

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

The description provides explicit billing context (1 credit per 10 filters, max 10) and warns to use totalSubscribers not totalFollowers for count thresholds. It explains YouTube-only fields and pagination response fields (creatorList, totalResults, hasNextPage, nextOffset) with instructions to pass nextOffset for pagination. While it doesn't explicitly state when not to use this tool, the platform context gives clear alternatives.

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

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

With no annotations, the description fully carries behavioral disclosure. It details response structure, a deferred filter (returns 400), semantic nuance about publishTime filter value vs response field, and pagination fields. This provides rich behavioral transparency.

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

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

The description is long but each sentence adds valuable information. It is well-structured: purpose, differentiation, response fields, filter fields, and additional notes. Slightly verbose but not wasteful.

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 5 parameters, the description thoroughly covers response format, filterable fields, pagination, and credit cost. The tool's complexity is well addressed, leaving no major gaps.

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

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

Schema coverage is 100%, but the description adds significant value: lists many filterable fields, notes a deferred filter, clarifies publishTime semantic split, and explains pagination parameters. This goes well beyond the schema's descriptions.

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

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

The description clearly states it searches individual YouTube content (videos, shorts, streams) and explicitly differentiates from sibling 'search_youtube' which searches creators. The verb 'search' and resource 'YouTube content' are specific and unambiguous.

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

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

The description explicitly distinguishes from search_youtube ('Different from search_youtube (which searches CREATORS)') and notes that the 4-day fresh-content exclusion does not apply here. While it doesn't explicitly state when not to use, the differentiation is strong and covers 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?

Without annotations, the description fully discloses behavioral traits: cost implications (1 credit on acceptance, 0 on duplicate/rejection) and rate limits (100/day per API key). It also details the return values and statuses. No contradictions with annotations (none provided).

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

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

The description is two sentences, front-loaded with the core purpose and return values, then adds cost and rate-limit details. Every sentence adds unique value with no redundancy.

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

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

With no output schema, the description provides essential return fields. It covers cost and rate limits. It could mention error handling or validation of inputs but is sufficient for a submission 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 each parameter described in the schema. The description does not add extra meaning beyond the schema; it only repeats the return values. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the action ('Submit a brand for indexing') and the resource (brand). It specifies the return values, and the sibling tools are all read-only queries, so this tool's distinct purpose is evident.

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 indexing new brands, and the sibling tools are all get/list/search operations, making the contextual choice clear. However, it does not explicitly state when not to use it or provide direct comparisons to alternatives.

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