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fledgeling-co

YouTube Transcript MCP

youtube_transcript

Retrieve YouTube transcripts from video captions or AI fallback. Identifies whether the transcript is human-written or machine-generated.

Instructions

Get a YouTube video's transcript. Tries the video's own captions first (free, and most videos have them), then Gemini reading the URL (billed), then yt-dlp if installed. Returns the transcript plus which source produced it, so you can tell a human-written caption track from a machine transcription. Accepts any YouTube URL shape or a bare video id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesA YouTube URL (watch, youtu.be, shorts, embed) or a bare 11-character video id.
allowPaidNoAllow the billed Gemini fallback when captions are missing or unusable. Defaults to true. Set false to get captions or nothing, which is the right choice when cost matters more than coverage.
languagesNoPreferred caption languages as BCP-47 codes, most wanted first, e.g. ["en","fr"]. Defaults to English.
timestampsNoPrefix each line with [mm:ss]. Only possible from a caption track; a model transcription has no timings.
Behavior5/5

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

The description discloses the tool's behavioral traits beyond annotations: it tries multiple sources in order, may incur billing via Gemini, uses yt-dlp conditionally, and returns the source for transparency. This adds significant context that annotations alone do not provide, though it does not contradict any annotation.

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

Conciseness5/5

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

The description is three sentences, front-loads the main purpose, and packs in essential behavioral details without fluff. Every sentence provides value, from the fallback chain to the output source indication.

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

Completeness4/5

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

The tool has four parameters and no output schema, but the description covers the key runtime behavior, billing, fallback logic, and return value. It does not describe the exact transcript format, but given the tool's simplicity and strong schema coverage, this is a minor gap.

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 description coverage is 100%, so parameters are already well documented. The description adds minor nuance about URL shapes and timestamps being only available from caption tracks, but this does not materially improve on the schema's own descriptions. Baseline 3 is appropriate.

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?

The description opens with 'Get a YouTube video's transcript', a specific verb+resource action that clearly states the tool's primary function. It differentiates itself from sibling tools by detailing the fallback chain and source attribution, making its unique role evident.

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

Usage Guidelines3/5

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

The description implies usage context through the fallback chain (captions, Gemini, yt-dlp) and mentions billing implications, but it does not explicitly state when to use this tool over alternatives like youtube_transcripts or youtube_transcript_doctor. There is no clear when-to-use vs when-not-to-use guidance.

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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