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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'fetches details' but doesn't clarify what details are included (e.g., statistics, branding, content), whether it's a read-only operation, potential rate limits, authentication needs, or error handling. This is inadequate for a tool with no annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.

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

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

For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'details' are returned (e.g., channel metadata, videos, playlists), leaving the agent uncertain about the tool's output. Given the complexity implied by sibling tools and lack of structured data, more context is needed.

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 description coverage is 100%, with clear documentation for both parameters (channelId and lang). The description adds no additional parameter information beyond what's in the schema, such as examples or edge cases. Given the high schema coverage, 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 ('fetches details') and resource ('YouTube channel'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'search_for_channels' or 'list_channel_videosshortslive', which also involve channel-related operations, so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (e.g., 'search_for_channels', 'list_channel_videosshortslive'), there's no indication of whether this is for basic metadata retrieval, detailed analytics, or other specific use cases, leaving the agent to guess based on tool names alone.

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 but offers minimal behavioral insight. It states the operation is a fetch (implying read-only), but doesn't cover authentication needs, rate limits, error handling, or response format. The mention of 'endpoint' hints at an API call but lacks operational details crucial for an agent.

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

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

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured by separating functional scope from behavioral notes.

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

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

For a tool with 3 parameters, no annotations, and no output schema, the description is inadequate. It lacks details on authentication, rate limits, error responses, and the structure of returned details. The agent would struggle to use this effectively without guessing at behavioral aspects.

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

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

Schema description coverage is 100%, so the schema fully documents parameters. The description adds no parameter-specific information beyond implying 'playlistId' is required. It doesn't explain the purpose of 'videos' (empty in example) or clarify 'lang' beyond what's in the schema. Baseline 3 is appropriate as the schema handles parameter documentation.

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 ('fetches details') and resource ('YouTube playlist'), including the types of playlists covered (user created, album, radio). It distinguishes from siblings like 'list_playlist_videos' by focusing on metadata rather than content listing. However, it doesn't explicitly contrast with 'get_channel_details' or 'get_video_details_price_1_3' beyond the resource type.

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

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

No explicit guidance on when to use this tool versus alternatives. While the description implies it's for playlist metadata, it doesn't specify when to choose this over 'list_playlist_videos' (for content) or 'search_for_playlists' (for discovery). There's no mention of prerequisites, error conditions, or integration with other tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a read operation ('fetches'), but lacks details on permissions, rate limits, error handling, or response format. This is inadequate for a tool with potential API constraints, though it doesn't contradict any annotations.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality, making it easy for an agent to parse quickly.

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

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

Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'details' include (e.g., text, images, metrics) or behavioral aspects like authentication needs. For a tool fetching data from a complex API like YouTube, this leaves significant gaps for an agent.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters (postId and lang). The description adds no parameter-specific information beyond what the schema provides, such as examples or constraints, resulting in a baseline score of 3.

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

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

The description clearly states the action ('fetches details') and resource ('YouTube community post'), making the purpose unambiguous. However, it doesn't differentiate this tool from sibling tools like 'get_channel_details' or 'get_video_details_price_1_3', which follow a similar pattern for different YouTube resources.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_channel_posts_pollvideoimage' for listing posts or 'list_post_comments' for post comments, leaving the agent to infer usage context from the tool name alone.

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 of behavioral disclosure. It mentions fetching details but doesn't cover critical aspects like rate limits, authentication requirements, error handling, or quota implications (e.g., from 'urlAccess' parameter). The description lacks behavioral context beyond the basic operation, leaving gaps for an agent to understand usage constraints.

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

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

The description is a single, well-structured sentence that efficiently lists the key resources fetched. It avoids unnecessary words and front-loads the core action ('fetches full details'), making it easy to parse. However, it could be slightly more concise by omitting redundant phrasing like 'as well as'.

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 (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return format, error conditions, or behavioral traits like quota usage mentioned in the schema. For a tool with rich parameters and no structured safety hints, the description should provide more context to guide 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?

Schema description coverage is 100%, providing detailed documentation for all parameters (e.g., 'videoId', 'urlAccess' with quota unit info, defaults for 'lang', 'videos', 'audios'). The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting without extra value from the description.

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

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

The description clearly states the tool's purpose: 'fetches full details of a YouTube video' with specific resources listed (URLs of videos, audios, thumbnails, subtitles, related videos, and playlists). It distinguishes from siblings like 'get_channel_details' or 'get_playlist_details' by focusing on videos, but doesn't explicitly differentiate from 'list_related_videos_and_playlists' which might overlap in functionality.

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

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, compare to sibling tools like 'list_related_videos_and_playlists' or 'search_for_videos_movies', or specify scenarios where this detailed fetch is preferred over simpler options. The description only states what it does, not when to use it.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that it 'lists autocomplete predictions depending on the keyword,' which implies a read-only operation but lacks details on rate limits, authentication needs, response format, or potential side effects. For a tool with no annotations, this is insufficient to inform safe and effective use.

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

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

The description is a single, efficient sentence that directly states the tool's function. It is front-loaded with the core action and resource, with no wasted words. However, it could be more structured by including context or usage hints, but it remains appropriately concise for its 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 no annotations, no output schema, and a tool that likely returns dynamic data (autocomplete predictions), the description is incomplete. It does not cover behavioral aspects like response format, error handling, or integration with sibling tools. For a tool with moderate complexity and lack of structured data, more context is needed to ensure 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?

Schema description coverage is 100%, so the schema already documents all parameters (keyword, lang, region) with descriptions. The description adds no additional meaning beyond implying the keyword drives predictions, but it does not explain how parameters interact or affect results. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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

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

The description states the tool 'lists autocomplete predictions depending on the keyword,' which provides a vague purpose. It specifies the verb 'lists' and resource 'autocomplete predictions' but lacks specificity about what domain or context these predictions are for (e.g., search suggestions, content recommendations). It does not distinguish from siblings like 'search_for_videos_movies' or 'list_items_with_next_token,' which could involve similar listing functions.

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

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

The description offers no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or specific contexts (e.g., for search suggestions vs. content discovery). Given siblings like 'search_for_videos_movies' and 'list_items_with_next_token,' there is no indication of when this autocomplete tool is preferred, leaving usage unclear.

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 of behavioral disclosure. It adds some context: 'Pagination scraping is supported' indicates handling of large data sets and the use of 'nextToken' for pagination. However, it fails to disclose critical traits such as whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or what the output format looks like. For a tool with no annotations and multiple parameters, this is a significant gap in transparency.

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

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

The description is appropriately sized with two sentences that convey key information: the main purpose and pagination support. It is front-loaded with the core functionality, and there is no wasted text. However, it could be slightly more structured by explicitly separating usage notes, but it remains efficient and clear.

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

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

Given the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and pagination but lacks details on behavioral traits (e.g., read-only status, error handling), output format, and usage guidelines. Without annotations or an output schema, the description should provide more context to be fully helpful for an AI agent, but it falls short.

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%, meaning all parameters are documented in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain 'channelId' formats or 'type' options in more detail). With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.

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: 'lists playlists, releases or podcasts of a YouTube channel.' It specifies the verb ('lists') and resource ('playlists, releases or podcasts of a YouTube channel'), which is clear and specific. However, it does not explicitly differentiate from sibling tools like 'list_channel_videosshortslive' or 'list_playlist_videos', which list different content types, so it lacks sibling differentiation for a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'Pagination scraping is supported,' which hints at usage for large datasets, but does not specify contexts, exclusions, or compare to sibling tools like 'list_channel_posts_pollvideoimage' or 'search_for_channels.' Without explicit when/when-not instructions or named alternatives, it offers minimal usage guidance.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'pagination scraping is supported,' which adds some context about handling large data sets. However, it lacks details on rate limits, authentication needs, error conditions, or what the output looks like (since no output schema exists). For a tool with no annotations, this is insufficient.

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

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

The description is concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds useful context about pagination. There's no wasted text, and both sentences earn their place by providing distinct information.

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

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

Given the tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on output format, error handling, authentication, and usage scenarios. Without an output schema, the description should ideally hint at return values or structure, but it doesn't. This leaves significant gaps for an agent to understand the tool fully.

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

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

The input schema has 100% description coverage, so the schema already documents all parameters (channelId, lang, nextToken). The description doesn't add any meaning beyond what's in the schema—it doesn't explain parameter interactions, defaults, or usage examples. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

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: 'lists poll, video, or image posts of a YouTube channel.' It specifies the resource (YouTube channel posts) and content types (poll, video, image). However, it doesn't explicitly differentiate from sibling tools like 'list_channel_videosshortslive' or 'get_post_details,' which reduces clarity about when to use this specific tool.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'pagination scraping is supported,' which hints at usage for large datasets, but doesn't specify scenarios, prerequisites, or exclusions. Without explicit when/when-not instructions or named alternatives, users must infer usage from the purpose alone.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'Pagination scraping is supported,' which adds some behavioral context (handling multiple pages of data). However, it lacks details on rate limits, authentication needs, error handling, or what the output looks like (e.g., format, fields). For a tool with no annotations, this is insufficient.

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

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

The description is very concise—two sentences that directly state the tool's function and a key feature (pagination). It is front-loaded with the core purpose and wastes no words, making it easy to scan and understand quickly.

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

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

Given the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks information on output format, error conditions, usage examples, and how it differs from sibling tools. For a tool that lists content with pagination and multiple parameters, more context is needed to guide 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?

Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description does not add any meaning beyond the schema (e.g., it doesn't explain parameter interactions or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'lists videos, shorts or live streams of a YouTube channel.' It specifies the resource (YouTube channel content) and the action (list), but does not explicitly differentiate it from sibling tools like 'list_channel_playlistsreleasespodcasts' or 'list_playlist_videos', which handle different content types or sources.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'Pagination scraping is supported,' which hints at usage for large datasets, but does not specify scenarios, prerequisites, or compare it to siblings like 'search_for_channel_videos_and_playlists' or 'list_items_with_next_token' for similar functionality.

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 pagination behavior ('Pagination scraping is supported'), which is valuable beyond the input schema. However, it omits other behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotations, this leaves gaps in understanding operational constraints.

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

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

The description is appropriately sized with two sentences: one stating the purpose and another adding behavioral context (pagination). It's front-loaded with the core function. However, the second sentence could be more integrated, and there's minor room for tightening (e.g., combining ideas), but it's efficient with zero 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 no annotations, no output schema, and 4 parameters with full schema coverage, the description is moderately complete. It covers the purpose and pagination but lacks details on response format, error cases, or usage nuances. For a list tool with pagination, more context on result structure or limitations would enhance completeness, making it adequate but with 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 description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain hashtag format beyond 'without #' or pagination details). With high schema coverage, the baseline is 3, as the description doesn't compensate with extra insights.

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: 'lists YouTube videos related to the hashtag.' It specifies the resource (YouTube videos) and verb (lists), and distinguishes it from siblings like search_for_videos_movies by focusing on hashtag-based retrieval. However, it doesn't explicitly differentiate from list_related_videos_and_playlists or other list tools, keeping it from a perfect score.

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 hashtag-related video listing, with 'Pagination scraping is supported' hinting at large result sets. It doesn't provide explicit when-to-use guidance versus alternatives like search_for_videos_movies or list_related_videos_and_playlists, nor does it mention exclusions. The context is clear but lacks sibling differentiation.

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

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

With no annotations provided, the description carries full burden. It discloses the technical behavior of handling large nextTokens via request body, which is valuable. However, it doesn't mention rate limits, authentication requirements, error conditions, response format, or whether this is a read-only operation. For a pagination tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

Two clear sentences that efficiently explain the tool's unique value proposition. The first sentence states the purpose, the second explains the technical advantage. No wasted words, though it could be slightly more specific about what 'items' means in this API.

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 is incomplete. It explains the technical mechanism for large tokens but doesn't cover what the tool returns, error handling, authentication needs, or rate limits. For a pagination tool in a media API context, users need to know what kind of items are returned and the response structure.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'any type of nextToken' but doesn't clarify what that means beyond the schema's explanation. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'list items with any type of `nextToken`' and explains the technical mechanism for handling long tokens. It distinguishes from siblings by focusing on pagination with large tokens, unlike other list/search tools that may use different pagination methods. However, it doesn't specify what 'items' refers to in this API context.

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

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

The description implies usage context: when you need to handle pagination with nextTokens longer than 4KB. It mentions this bypasses URL/header size limits, suggesting when to use this vs standard pagination. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the 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 provided, the description carries the full burden. It discloses that unavailable videos won't be listed and thumbnails won't be blurred by age safety, adding useful behavioral context. However, it doesn't cover other potential traits like rate limits, authentication needs, error handling, or what 'pagination scraping' entails operationally, leaving gaps for a tool with pagination and language parameters.

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

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

The description is concise with three sentences that each add value: stating the core function, pagination support, and thumbnail behavior. It's front-loaded with the main purpose. However, the second sentence about pagination could be integrated more smoothly, and it lacks structural markers like bullet points for clarity.

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 context but is incomplete. It covers what the tool does and some behavioral aspects, but misses details on return values, error cases, or full usage scenarios. For a tool with pagination and language parameters, more guidance on output structure or limitations would enhance completeness.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (playlistId, lang, nextToken) with examples and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'nextToken' interacts with 'playlistId' or the implications of 'pagination scraping'. Baseline 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the tool lists videos from a YouTube playlist, specifying 'available' videos and mentioning unavailable ones won't be listed. It distinguishes from siblings by focusing on playlist videos rather than channel details, posts, or searches. However, it doesn't explicitly differentiate from 'list_channel_videosshortslive' or 'list_items_with_next_token' in terms of scope.

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

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

The description implies usage for retrieving playlist videos with pagination and language support, but doesn't explicitly state when to use this tool versus alternatives like 'list_channel_videosshortslive' or 'get_playlist_details'. It mentions 'pagination scraping is supported', which hints at handling large datasets, but lacks clear exclusions or comparative guidance with 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 provided, the description carries full burden for behavioral disclosure. It mentions 'pagination scraping is supported' which adds some context about handling multiple pages of data, but fails to describe other important behaviors like rate limits, authentication requirements, error conditions, or what the response format looks like (especially critical since there's no output 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 appropriately concise with just two sentences that each serve a purpose - one states the core functionality, the other adds important behavioral context about pagination. It's front-loaded with the main purpose and wastes no words on 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 the complexity of a comment listing tool with pagination support, no annotations, and no output schema, the description is insufficient. It should explain the response format, error handling, rate limits, and how pagination works in practice. The mention of pagination is helpful but doesn't provide enough context for an agent to use the tool effectively.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters, provide usage examples, or clarify edge cases. Baseline 3 is appropriate when 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 verb 'lists' and resource 'comments of a YouTube community post', making the purpose specific and understandable. However, it doesn't explicitly differentiate from the sibling tool 'list_video_comments', which might cause confusion about when to use each tool for different comment types.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'list_video_comments' or other sibling tools. It mentions pagination support but doesn't explain when pagination should be used or what scenarios require this specific tool over others in the server.

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 effectively describes pagination behavior ('Pagination scraping is supported') and clarifies the relationship between videoId and nextToken parameters. However, it doesn't mention rate limits, authentication requirements, or error handling.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, while the second provides crucial usage guidance. There's zero wasted text and the information is front-loaded appropriately.

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 paginated list tool with no output schema, the description provides good context about the pagination workflow and parameter relationships. However, it doesn't describe the return format or structure of the related videos/playlists, which would be helpful given the absence of an output schema.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond what's in the schema - it only implies the pagination relationship between videoId and nextToken. Baseline 3 is appropriate when 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 tool's purpose with specific verbs ('lists related videos and playlists') and identifies the resource ('YouTube video'). It distinguishes this tool from siblings like 'get_video_details' by focusing on related content rather than video metadata.

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

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

The description provides explicit guidance on when to use this tool versus alternatives. It recommends getting the first page via 'Video > Get Video Details' and then using this tool for subsequent pages, creating a clear workflow distinction from 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 the full burden. It discloses that 'Pagination scraping is supported,' which adds some behavioral context beyond the input schema. However, it lacks details on rate limits, authentication needs, error handling, or what the output looks like (e.g., comment structure), leaving significant gaps for a tool with no annotations.

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

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

The description is concise with two sentences that directly address the tool's function and a key feature (pagination). It is front-loaded with the main purpose, though it could be slightly more structured by explicitly separating core functionality from additional features.

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 (4 parameters, no annotations, no output schema), the description is incomplete. It lacks information on output format (e.g., what data is returned), error conditions, or detailed behavioral traits like rate limits. While it mentions pagination, this is insufficient for full contextual understanding, especially without annotations.

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 description coverage is 100%, meaning all parameters are documented in the schema. The description does not add any additional meaning or context about the parameters beyond what the schema provides, such as examples or usage tips. Thus, it meets the baseline of 3 for high schema coverage.

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

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

The description clearly states the tool's purpose: 'lists comments of a YouTube video.' It specifies the resource (YouTube video comments) and the action (list), but does not explicitly differentiate it from sibling tools like 'list_post_comments' or 'list_items_with_next_token,' which prevents a score of 5.

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 minimal guidance: it mentions 'Pagination scraping is supported,' which hints at usage for handling multiple pages, but offers no explicit advice on when to use this tool versus alternatives like 'list_post_comments' or 'list_items_with_next_token,' nor does it specify prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'pagination scraping is supported' which adds some context about how results are returned, but doesn't describe what the tool returns (channel objects? metadata?), error conditions, rate limits, authentication requirements, or whether it's a read-only operation. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is extremely concise with just two sentences that both add value. The first sentence states the core purpose, and the second adds important behavioral context about pagination. There's zero wasted text or redundancy.

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

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

Given that there are no annotations and no output schema, the description should do more to compensate. While it's concise, it doesn't explain what the tool returns (channel data format), error handling, typical use cases, or how it differs from sibling tools. For a search tool with 4 parameters and no structured output documentation, this leaves the agent with insufficient context.

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

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

Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to the rubric, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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

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

The description clearly states the tool searches for YouTube channels with 'searches for YouTube channels', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'search_for_channel_videos_and_playlists' or 'search_for_playlists', which also perform searches but for different resource types.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions pagination support but doesn't specify when to use this search tool over other search tools (like for videos or playlists) or when to use it versus list tools for channels. There's no mention of prerequisites, limitations, or typical 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Pagination scraping is supported,' which adds some context about handling large result sets, but fails to cover critical aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., format, structure). For a search tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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

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

The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose and a key feature (pagination). There's no wasted text, and it efficiently communicates essential information without unnecessary elaboration.

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

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

Given the complexity of a search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., rate limits, auth), output format, and usage guidelines compared to siblings. While concise, it doesn't provide enough context for an agent to fully understand how to invoke and interpret results effectively.

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

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

The input schema has 100% description coverage, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify parameter interactions or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'searches for videos and playlists in a YouTube Channel.' It specifies the resource (YouTube Channel) and the action (search for videos and playlists). However, it doesn't explicitly differentiate from sibling tools like 'search_for_channels' or 'search_for_videos_movies,' which reduces clarity on when to choose this specific tool.

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

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

The description provides minimal guidance: it mentions 'Pagination scraping is supported,' which hints at usage for large datasets, but offers no explicit advice on when to use this tool versus alternatives like 'search_for_channels' or 'search_for_videos_movies.' There's no context on prerequisites, exclusions, or comparative scenarios, leaving the agent with little direction.

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

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

No annotations are provided, so the description carries the full burden. It adds some behavioral context: pagination scraping is supported, and thumbnails won't be blurred by age safety. However, it lacks details on rate limits, authentication needs, error handling, or what the response looks like (e.g., format, fields). This is a moderate disclosure but incomplete 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 concise with two sentences that are front-loaded: the first states the core purpose, and the second adds behavioral details. There's no wasted text, but it could be slightly more structured (e.g., bullet points for features).

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, 4 parameters with full schema coverage, and no output schema, the description is moderately complete. It covers the purpose and some behavioral traits but lacks output details, error handling, and usage guidelines. For a search tool with pagination, this is adequate but has clear 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 description coverage is 100%, so the schema already documents all parameters (keyword, lang, sortBy, nextToken) with descriptions. The description doesn't add any meaning beyond what the schema provides, such as examples or usage tips. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool searches for YouTube playlists, which is a specific verb (search) and resource (YouTube playlists). It distinguishes from siblings like search_for_channels and search_for_videos_movies by specifying the playlist resource type. However, it doesn't explicitly contrast with list_channel_playlistsreleasespodcasts or list_playlist_videos, which are more about listing than searching.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over sibling tools like search_for_channels or list_channel_playlistsreleasespodcasts, nor does it specify prerequisites or exclusions. The only implied usage is for searching playlists, but no context is given.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about pagination scraping support and that thumbnails won't be blurred by age safety, which aren't obvious from the schema. However, it doesn't mention rate limits, authentication requirements, error conditions, or what the response format looks like (e.g., what data is returned). For a search tool with 7 parameters, this leaves significant gaps.

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

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

The description is concise with three sentences that each add value: stating the core function, mentioning pagination support, and noting thumbnail behavior. It's front-loaded with the main purpose. However, the second sentence about pagination scraping could be clearer (e.g., explaining how it works), and there's some redundancy with 'YouTube videos (movies)'.

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

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

Given the tool's complexity (7 parameters, search functionality) and lack of annotations or output schema, the description is moderately complete. It covers the core purpose and some behavioral traits but misses critical details like response format, error handling, and usage guidelines relative to siblings. The schema handles parameters well, but the description doesn't compensate for other 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 description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or provide examples. The baseline score of 3 reflects adequate coverage from the schema alone, with no value added by the description.

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

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

The description clearly states the tool searches for YouTube videos (movies) with optional filters, providing a specific verb ('searches') and resource ('YouTube videos/movies'). It distinguishes from siblings like 'search_for_channels' and 'search_for_playlists' by specifying the content type. However, it doesn't explicitly differentiate from 'search_for_channel_videos_and_playlists' or 'list_hashtag_videos', which are similar video 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?

The description provides no guidance on when to use this tool versus alternatives like 'search_for_channels', 'search_for_playlists', or other video listing tools. It mentions optional filters and pagination scraping, but doesn't specify use cases, prerequisites, or exclusions. The agent must infer usage from the tool name and context alone.

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

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

No annotations are provided, so the description carries the full burden. It discloses the tool's behavior by mentioning the prerequisite call and the multi-functionality (translate, convert, download), but lacks details on side effects (e.g., if it modifies data, requires authentication, or has rate limits). It adequately covers the basic operation but misses deeper behavioral traits.

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

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

The description is highly concise and front-loaded, consisting of two sentences that directly state the tool's purpose and usage prerequisite. Every sentence earns its place by providing essential information without redundancy or unnecessary details.

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

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

Given the tool's complexity (multi-function with 4 parameters) and no annotations or output schema, the description is reasonably complete. It covers the purpose and prerequisite well, but could improve by hinting at output behavior (e.g., what is returned) or error handling. It's sufficient for basic use but has minor gaps.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'fixOverlap' or provide extra context for 'targetLang'). Baseline 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('translate, convert and download') and resource ('subtitle of a YouTube video'), distinguishing it from sibling tools that focus on retrieval (e.g., 'get_video_details') or listing operations. It explicitly outlines the multi-step functionality, making it distinct 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 guidance on when to use this tool by specifying a prerequisite: 'Before doing this, please call endpoint `Video > Get Video Details` to obtain subtitle URLs.' This directly informs the agent of the necessary context and sequence, with no ambiguity about alternatives or exclusions.

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