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mrsknetwork

YouTube MCP

by mrsknetwork

get_video_transcript

Read-only

Download complete spoken transcripts from YouTube videos as plain text using yt-dlp, no API key required. Supports language preference and automatic fallback for content research.

Instructions

Download the full spoken transcript of a YouTube video as clean plain text using yt-dlp — no API credentials required. Tries manual English captions first (or specified language_code), then auto-generated, then falls back to the first available language. Returns concatenated plain text plus metadata about which language and caption type was used. Ideal for content research without consuming YouTube API quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_idYesThe ID of the YouTube video to download the transcript for
end_minutesNoEnd time in minutes from the beginning of the video.
language_codeNoPreferred language code (e.g., 'en', 'es', 'fr', 'ja'). Defaults to English if not specified, then falls back to first available language.
prefer_manualNoIf true (default), prefer manually created captions over auto-generated ones when both are available.
start_minutesNoStart time in minutes from the beginning of the video.
max_charactersNoMaximum character length of the returned transcript to prevent context limit errors. Defaults to 100000. Set to 0 or a very large number for unlimited.
Behavior4/5

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

Annotations provide readOnlyHint=true. The description adds value by detailing the fallback order (manual captions, auto-generated, first available) and language handling, which is beyond annotations. It does not mention potential side effects or rate limits, but openWorldHint covers external interactions.

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?

The description is moderately concise, with the main purpose front-loaded. It contains a few extra words ('using yt-dlp', 'no API credentials required') that are helpful but not excessive. Structure is logical with fallback and use case.

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?

Given 6 parameters, full schema coverage, and no output schema, the description adequately covers the tool's operation. It explains the return format (concatenated plain text plus metadata) and fallback logic. Could mention potential truncation via max_characters but is already sufficient.

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 baseline is 3. The description adds no extra semantic context beyond what the schema already describes for parameters like language_code, start_minutes, etc. It reiterates the fallback for language_code but without new details.

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 clearly states the tool downloads the full spoken transcript of a YouTube video as clean plain text, using yt-dlp without API credentials. It distinguishes from sibling tools (e.g., list_video_captions) by focusing on content retrieval rather than metadata listing.

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?

The description indicates when to use (content research without API quota) and implies a fallback strategy. However, it does not explicitly state when not to use it or compare with alternatives like list_video_captions for metadata needs.

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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