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

Annotations are absent, so the description carries the full burden. It discloses the return shape across tabs and mentions pagination token availability indirectly via the parameter, but it does not describe rate limits, errors, or the fact that the operation is read-only (though 'Fetches' implies it). The behavioral disclosure is adequate but not exhaustive for a tool with 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?

The description is well-structured, starting with a concise summary ('Get YouTube Channel Data') and then expanding into function and use cases. Though it is longer than minimal, every sentence adds relevant information—the tab enumeration, return fields, and practical applications. It is front-loaded and avoids fluff, earning a 4.

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 structure, which it does in detail by listing channel identity fields and tab-specific content types. It also covers the main use cases and even hints at integration with other endpoints. It does not cover pagination behavior beyond mentioning the token, but the schema already documents the parameter. Overall, the description is comprehensive for the tool's 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?

The input schema already provides 100% description coverage for all parameters, including detailed explanations for both `tab` and `channelId`. The description adds value by contextualizing the tool's use cases (e.g., feeding IDs to other endpoints) but does not contribute new parameter-specific semantics beyond the schema. Baseline 3 is appropriate given 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 opens with a specific verb ('Fetches') and resource ('YouTube channel by ID or handle'), enumerates the supported tabs, and details the returned data (channel identity, counts, social links, tab items). It also clearly distinguishes this tool from siblings by noting it can 'feed videoId into the YouTube Video / Transcript endpoints.' This leaves no ambiguity about what the tool does or how it differs from search, transcript, and video 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 clear use cases (monitor competitor channels, build directories, track growth signals) and implies when to use it relative to the video/transcript endpoints. However, it does not explicitly state when NOT to use this tool or directly compare it to the sibling search tool. The context is clear and actionable, but exclusions are left implicit.

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

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

No annotations are provided, so the description carries the full burden — and it delivers. It discloses that the tool returns the complete results page split into distinct blocks (videoResults, channelResults, shorts, shelves, ads), includes advertising/sponsored content, and preserves positionOnPage for layout reconstruction. This is meaningful behavioral context beyond a generic "search" claim. It does not mention rate limits or auth requirements, but the return-structure and composition disclosures are the most decision-relevant behaviors for a search 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 long but every sentence earns its place: the core purpose comes first, followed by a dense but necessary enumeration of return blocks and their distinguishing fields, then a targeted use-case sentence. The block listing is justified because there is no output schema to carry that information. Minor redundancy ('Get YouTube Search Results' repeated as title) keeps it from a 5.

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 shoulders the return-value burden — and it does so thoroughly, naming each result category and key fields (views, position, advertiser, subscribers, verified flag). Combined with 100% schema description coverage for the 11 parameters, an agent has everything needed to select and invoke this tool correctly. The only gaps are operational details such as rate limits, which are not covered by annotations either.

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 schema itself is exceptionally detailed (per-enum explanations for length, sortBy, filters__, raw sp-token override semantics). The description adds minimal parameter instruction beyond the schema — its only param-adjacent contribution is the hint about feeding returned videoId/channelId into sibling endpoints. Per the baseline rule, 3 is correct 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 opens with a specific verb+resource pair — "Searches YouTube for a query" — and precisely scopes the tool as returning the full results page. It differentiates from sibling tools (channel, transcript, video endpoints) by defining this as the search/entry point that produces results blocks. An agent can immediately recognize what this tool does and what it is not.

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 explicit use cases — "competitor and topic monitoring, brand-mention tracking, ad-placement intelligence, creator discovery, trend research" — and even tells the agent what to do afterward: "feed videoId / channelId into the YouTube Video, Channel, or Transcript endpoints." This directly routes the agent between siblings, stating when this tool is the right choice and when to move on.

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 bears full responsibility for behavioral transparency. It thoroughly describes the response structure: timed segments with startMs, endMs, snippet, and startTimeText, plus availableTranscripts with language details and selection status. It also explains the effect of type=asr, which goes beyond basic schema. Minor omissions like error handling or rate limits are not mentioned, but the core behavior is well 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 longer than a minimal example but each sentence serves a purpose: the first states the core function, the middle details response structure, and the final lists concrete use cases. It is front-loaded with the purpose and structured logically. It could be trimmed slightly, but it is not wasteful and earns its length given the lack of an output schema.

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 remarkably complete picture: what parameters control, what the response contains, and common application contexts. It even suggests integration with another endpoint for cross-video analysis. The only missing elements are error/edge-case behavior and explicit rate-limit or authentication notes, but these are outside typical description scope for a simple GET 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?

The input schema already provides 100% coverage with detailed descriptions for v, type, and languageCode, including format examples and behaviors when omitted. The tool description largely repeats this information (e.g., 'languageCode selects the track', 'type=asr requests auto-generated'), adding no significant meaning beyond what the schema already states. This meets the baseline 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 opens with 'Get YouTube Video Transcript' and explicitly states the resource (transcript of a YouTube video by 11-character videoId). It clearly distinguishes this tool from sibling tools like channel or search by focusing purely on retrieving transcript content for a given video, so an agent can immediately understand its unique function.

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

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

The description provides explicit usage scenarios ('Use to feed a video's spoken content into RAG/LLM pipelines, generate summaries...') and even suggests pairing with the YouTube Channel endpoint to enumerate videos. However, it does not explicitly state when NOT to use this tool or name alternative siblings for exclusion, leaving slight ambiguity in choosing 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 of behavioral disclosure. It clearly states the operation is a read/fetch and details the extensive output fields. However, it does not mention potential failure modes (e.g., invalid videoId), rate limits, or any authorization requirements, which are relevant for a tool that likely sits behind an API key.

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 front-loaded with the core action and then the comprehensive field list, followed by practical use cases. The list of fields is dense but necessary given the tool's breadth. It is structured and readable, though the first sentence 'Get YouTube Video Data' is redundant with the title and could be trimmed. Overall it earns its length.

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

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

Given the complexity of the tool (many return fields) and the absence of an output schema, the description compensates by enumerating the response contents in detail. It also covers the main use cases and the relationship to the Transcript sibling. Missing are error handling and any limitations, but for an enrichment/read tool this is mostly sufficient.

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

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

The input schema covers all parameters (v, gl, hl, deviceType) with descriptive text, including valid values and examples. The rich schema description (100% coverage) means the tool description does not need to reiterate parameter meanings. It adds no extra parameter semantics beyond what the schema already provides.

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

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

The description clearly states the tool fetches a single YouTube video by its 11-character videoId and enumerates the specific data returned (title, thumbnails, views, likes, flags, channel info, captions, music matches, etc.). The verb 'Fetches' is specific and the resource is unambiguous, and the mention of the Transcript endpoint distinguishes it from a sibling 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 explicit use cases: enrichment, monitoring growth, extracting music/affiliate links, feature gathering for recommender/RAG pipelines, and detecting caption tracks before using the Transcript endpoint. It clearly indicates when to use this tool for those purposes, though it does not explicitly contrast it with the channel or search siblings beyond the transcript reference.

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