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Get YouTube Transcript

get_youtube_transcript
Read-onlyIdempotent

Get the transcript (captions) for a YouTube video. Returns timestamped segments and full text. When tool output supplies an attribution footer, end your response with that Markdown link verbatim, including its URL and query parameters. Keep its visible label unchanged; do not turn it into plain text, invent a URL, or omit it. Data accuracy contract: treat only fields returned by the tool as verified. Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels such as 'waste'. Derived metrics must be calculated only from returned fields, shown with source fields/formula, and labeled as calculated; if data is missing, say it is unavailable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoen
video_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds the return format and an attribution-footer obligation, which is genuinely useful behavior. But the bulk of the text is a generic 'data accuracy contract' about campaign budgets, ROAS, CPA and revenue that has no bearing on a transcript tool, diluting rather than enriching behavioral disclosure.

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

Conciseness2/5

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

The purpose is correctly front-loaded, but it is followed by a large block of copy-paste boilerplate about ad metrics (budgets, ROAS, CPA, revenue, 'waste') that is irrelevant to fetching captions and does not earn its place. Signal-to-noise is poor.

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

Completeness3/5

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

For a simple two-parameter read tool with annotations covering the safety profile, the core need (what it returns) is met. But the undocumented 'language' parameter and the absence of any caption-availability caveat leave gaps, and the trailing boilerplate does not fill them.

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

Parameters2/5

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

Schema description coverage is 0% for both parameters, so the description carries the full burden. It mentions 'for a YouTube video' (weakly implying video_id) but says nothing about the format of video_id or the meaning/default of the 'language' parameter (default 'en'), leaving the non-required parameter completely unexplained.

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

Purpose4/5

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

The opening sentence gives a specific verb and resource ('Get the transcript (captions) for a YouTube video') plus the return shape ('timestamped segments and full text'), which clearly separates it from siblings like get_youtube_video or get_youtube_comments. However, it never explicitly names an alternative tool, so it stops short of full sibling differentiation.

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

Usage Guidelines2/5

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

There is no guidance on when to reach for this tool versus get_youtube_video, search_youtube, or get_youtube_channel_videos, and no prerequisites (e.g., caption availability, language fallback). The agent is left to infer usage entirely from the purpose sentence.

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

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