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

Annotations already declare readOnlyHint and idempotentHint, so the description's job is to add behavioral context. It does detail failure codes and URL handling, which is helpful. However, there is an internal contradiction: the description says 'if the requested language is missing it fails with LANGUAGE_UNAVAILABLE plus the available list' while the languages parameter description says 'Falls back to the closest available track and says so in notes.' This inconsistency undermines trust and clarity.

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 reasonably concise for the amount of information it conveys. It front-loads the primary purpose and then covers failure modes and usage tips in a structured manner. It is not verbose, though it could be tightened by removing the contradictory language note.

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) and the lack of an output schema, the description covers URL formats, output options, failure modes, and truncation behavior (via maxChars in schema). However, the language fallback/failure contradiction leaves a gap that could confuse an agent. Overall, it is fairly complete but not perfect.

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

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

Schema coverage is 100% with detailed descriptions for all 7 parameters, so the baseline is 3. The description adds a small amount of guidance (e.g., explaining the difference between output=segments and timestamps=true) but does not significantly enhance parameter understanding beyond what the schema already provides. It also repeats the contradictory language behavior.

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

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

The description clearly states the tool's purpose: 'Return the transcript of a YouTube video, from any YouTube URL.' It enumerates the URL formats it accepts and explicitly contrasts failure modes (NO_TRANSCRIPT, LANGUAGE_UNAVAILABLE). This unambiguously differentiates it from sibling tools like youtube_parse_url or youtube_video_info, which serve different purposes.

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

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

The description provides usage guidance for output selection ('Use output=segments for timestamped cues, or timestamps=true for [mm:ss] prefixed lines') but does not explicitly state when to prefer this tool over its siblings. While the purpose is clear, there is no direct 'use this instead of X when...' guidance, though the context makes it obvious.

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?

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds useful output semantics: it flags auto-generated tracks and explains that hasCaptions=false means no transcript exists at all, which is beyond what annotations convey.

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

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

Three sentences, each earning its place: the core function, the usage trigger, and a key result field meaning. The most important action is front-loaded and there is no filler.

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

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

For a simple one-parameter, read-only tool with strong annotations and a fully documented schema, the description covers what the tool returns and when to invoke it. No critical information is missing for an agent to select and call it correctly.

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

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

The schema covers the single url parameter at 100%, including a thorough list of accepted URL formats. The description adds no additional parameter-level meaning, so the schema carries the burden and the baseline 3 applies.

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

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

The description uses a specific verb and resource: "List every caption track a video offers," and clarifies the auto-generated marking. It differentiates from youtube_get_transcript by positioning this as the survey of available tracks rather than the transcript content itself.

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 gives explicit trigger conditions: use after LANGUAGE_UNAVAILABLE or to decide what youtube_get_transcript can return. It names the relevant sibling tool and gives the agent a clear routing decision.

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?

Beyond the annotations, the description adds valuable behavioral detail: it is offline and 'Makes no network requests.' It also reveals what kind of recognition output the tool produces, which complements the safe-read/idempotent hints.

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

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

Three tight sentences with no filler. The core capability and offline guarantee are front-loaded, and the use-case sentence earns its place by clarifying when the tool is helpful.

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

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

For a single-parameter, offline, read-only parser with full schema coverage and helpful annotations, the description is complete. It explains what the tool parses, how it behaves, and why an agent would choose it.

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 the single url parameter well. The description adds context about valid link forms (playlist, channel, clip) but does not substantially extend the parameter meaning beyond what the schema states.

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 begins with a specific verb and resource: 'Offline parse of a YouTube reference.' It clearly enumerates the outputs (video id, playlist id, start offset, recognition method) which distinguishes it from transcript, language, and video-info siblings.

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 states the intended use: 'validating a link and explaining playlist, channel or clip links.' This gives clear context for when to call the tool, though it does not explicitly say when not to use it or name alternative 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds 'cheap' (performance expectation) and specifies the exact data returned, plus its diagnostic role. This adds context beyond annotations without contradiction, though it omits details like pagination or rate limits, which are not critical for a read-only info 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 two sentences, front-loaded with the core purpose and output, then the usage context. No wasted words; every sentence earns its place.

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

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

For a single-parameter read-only info tool with full schema coverage and annotations covering safety, the description is complete. It tells the agent what it returns, when to use it, and why, leaving nothing essential missing for correct invocation.

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

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

Schema coverage is 100% – the url parameter is thoroughly described in the schema, listing all supported URL formats. The description adds no additional parameter information, so the baseline 3 applies. It does not compensate for any schema gap because there is none.

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 action ('Cheap report for one video') with concrete output fields (title, author, duration, caption languages), clearly distinguishing it from siblings like youtube_get_transcript and youtube_parse_url. The purpose is unambiguous and the tool's role in the transcript workflow is explicit.

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

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

The description provides clear context: 'Call it before a batch of transcript requests, or to explain why a transcript is missing.' This tells the agent when to use it, though it does not explicitly name the alternative tools or state when not to use it (e.g., 'if you need the transcript text, use youtube_get_transcript'). The guidance is sufficient but lacks explicit exclusions.

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