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

With no annotations provided, the description carries the full burden, and it does well: it states what is fetched, which tabs are supported, what channel-level fields are returned, and how results can be reused. It does not mention rate limits, authentication, or explicit read-only status, but the verb 'Fetches' and the GET-style title make the operation's nature clear.

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 but well-organized: it starts with the core fetch behavior, then explains output fields, then lists concrete use cases. Every sentence adds value; only minor redundancy exists between the description's tab list and the schema's tab enum.

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

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

For a tool with no output schema and no annotations, the description provides a solid mental model of inputs, outputs, tabs, and downstream use. It could go deeper on pagination/response structure, but the schema already covers paginationToken, and the description sufficiently explains what an agent can expect from the result.

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 every parameter. The description reinforces channelId's accepted formats and explains the meaning of tab-level output, but it does not add significant meaning beyond the schema for gl, hl, deviceType, or paginationToken. 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 opens with a specific verb ('Fetches a YouTube channel by its ID or handle') and clearly distinguishes the tool from YouTube search, video, and transcript siblings by focusing on channel-level data and per-tab content. It names concrete output types, making the tool's 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?

The description gives clear application contexts ('monitor competitor channels, build creator/brand directories, track posting cadence and growth signals') and even routes downstream usage to the YouTube Video/Transcript endpoints. It does not explicitly list when not to use this tool versus search or video endpoints, but the use cases are concrete enough for an agent to decide.

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

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

The description makes clear this is a read-oriented search operation that returns a results page, but no annotations are present and it does not explicitly mention authentication, rate limits, error behavior, or confirm there are no side effects. The read-only nature is implied rather than fully 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 dense but every clause earns its place: the first sentence front-loads the purpose and output structure, and the second sentence explains real-world use cases and relationships to sibling tools. There is no redundant filler or repeated schema content.

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

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

Given there is no output schema, the description thoroughly covers the response shape (videoResults, channelResults, shortsResults, shelves, adsResults, sponsoredResults, positionOnPage) and gives downstream usage context. Pagination is also covered indirectly through the paginationToken parameter, making the tool self-contained enough for an agent.

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

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

The input schema already provides 100% descriptive coverage of all 11 parameters, including enums and the sp override behavior. The description itself adds no parameter-level meaning beyond that, so it sits at the baseline for a fully documented 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 opens with 'Searches YouTube for a query' and precisely identifies the resource and action, and it enumerates the distinct result sections returned. This makes the tool's purpose unmistakable and distinguishes it from the channel, video, and transcript sibling 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?

It explicitly lists concrete use cases (competitor monitoring, brand tracking, ad-placement intelligence, creator discovery, trend research) and tells agents to feed returned videoId/channelId into the Video, Channel, or Transcript endpoints. This provides clear guidance on when to use the search tool versus its siblings.

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

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

With no annotations, the description carries the full burden and does well by explaining the response shape: transcript segments with startMs/endMs/snippet/startTimeText, plus availableTranscripts[] with language/type/selected info. It does not cover error cases such as missing tracks or invalid video IDs, but the behavioral context is strong.

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 and front-loaded, leading with the core purpose before expanding into response details and use cases. Every sentence adds meaningful context, with no filler or repetition.

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

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

Despite having no output schema, the description thoroughly explains the return value structure and available track discovery. Combined with the complete parameter schema, an agent has enough information to select and invoke this tool correctly for transcript-related tasks.

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 describes all three parameters with 100% coverage, so the baseline is 3. The description reinforces languageCode and type=asr semantics but adds little beyond what the schema already states; the v parameter is already fully documented in the schema.

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

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

The description clearly states it returns the timed transcript (subtitles) of a YouTube video by 11-character videoId. This is a specific verb+resource that is naturally distinct from the sibling channel/search/video endpoints.

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 provides concrete use cases such as RAG/LLM pipelines, summaries, archives, translation, and accessibility workflows. It also suggests pairing with the YouTube Channel endpoint to enumerate videos, but it does not explicitly contrast with the search or video siblings.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly indicates a read-only fetch and provides rich output semantics: raw + normalized views/likes, safety/unlisted flags, captions with kind:asr, Content-ID music matches, social links, and related rails. It omits error handling, auth, and rate limits, but for a GET-style data tool the described behavior is substantially 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 long but information-dense: it front-loads the core purpose, then systematically lists return fields and use cases without filler. It could be structured into shorter sentences, but every clause adds relevant information for a tool with a rich payload.

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 compensates well by enumerating the full return surface and tying usage to the transcript sibling. Minor gaps remain around error cases and access requirements, but an agent has enough to invoke the tool correctly for typical metadata enrichment tasks.

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 all four parameters (v, gl, hl, deviceType) are already documented with types, examples, and allowed values. The description only repeats the videoId concept and does not add meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description states a specific verb ('Fetches') and resource ('a single YouTube video by its 11-character videoId') and enumerates the returned fields in detail. It is clearly distinct from the sibling channel/search/transcript tools because it is scoped to 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 lists concrete use cases: enriching video URLs, monitoring view/like growth, extracting music or affiliate links, building recommender/RAG features, and detecting caption tracks before calling the YouTube Transcript endpoint. It does not explicitly state when not to use the tool, but the context is clear enough for an agent to select it.

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