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Get a YouTube video transcript

youtube_transcript
Read-only

Returns a YouTube video's captions as JSON: timestamped segments (start, dur, text) and a joined text, plus availableLanguages, the picked language, isAutoGenerated, and the video's title, author and lengthSeconds. Pass a video id or any YouTube video URL; choose a caption language with lang. Ideal for LLM/RAG pipelines, subtitles and content analysis. Returns JSON. Billed like an API request; listing plugins bill one unit per result page they fetch, so keep max_results modest.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoPreferred caption language code, e.g. 'en'. Omit for the default/first track. A prefix like 'en' matches 'en-US'.
videoYesVideo id (dQw4w9WgXcQ) or any YouTube video URL.
formatNoOutput shape: segments, text. 'segments' returns timestamps; 'text' returns only the joined plaintext. Default: segments.
proxy_countryNoProxy country the request is sent from (ISO-2, e.g. 'US', 'DE') for the watch page. Defaults to US.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/openWorldHint/destructiveHint=false, and the description adds real context: the exact return shape, auto-generated caption flag, availableLanguages, and the billing model (one unit per API request). The trailing note about 'listing plugins bill one unit per result page they fetch, so keep max_results modest' references a parameter this tool does not have, which is stray noise rather than contradiction.

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

Conciseness4/5

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

One dense, well front-loaded paragraph that leads with the return contract before inputs and cost. It is efficient overall, but the billing sentence about listing plugins and max_results is irrelevant to this tool and costs it a point.

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

Completeness5/5

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

With no output schema, the description carries the full burden of describing return values and does so completely (segments with start/dur/text, joined text, language metadata, video metadata). Combined with full parameter coverage, an agent has everything needed to call it correctly.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents lang, video, format and proxy_country; the description only re-summarizes video (id or URL) and lang. Baseline 3 is appropriate since it adds minimal meaning beyond structured fields.

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

Purpose5/5

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

States a specific verb+resource (returns a YouTube video's captions) and enumerates the exact return fields, so it is unmistakable against youtube_search or fetch_html. An agent knows precisely what this produces without opening the schema.

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

Usage Guidelines4/5

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

Gives clear intake guidance ('Pass a video id or any YouTube video URL') and named use cases (LLM/RAG pipelines, subtitles, content analysis), plus a language-selection condition. It never names an alternative tool or states when not to use it, so it stops short of the top band.

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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