Provides tools for extracting YouTube video content, including titles, descriptions, metadata, and transcripts generated from subtitles or via speech-to-text processing.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@YouTube Content Extractor MCP Extract the transcript and metadata from https://www.youtube.com/watch?v=dQw4w9WgXcQ"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for extracting YouTube video content with transcript processing.
Video title and description
Transcript (from subtitles or Whisper speech-to-text)
Video metadata
Before installing this MCP server, you need to install the following dependencies:
yt-dlp (YouTube content extraction):
pip install yt-dlp
# or via Homebrew
brew install yt-dlpOpenAI Whisper (for audio transcription fallback):
pip install openai-whisper
# or via Homebrew
brew install openai-whisperCheck that all dependencies are properly installed:
yt-dlp --version
whisper --helpRelated MCP server: YouTube Insights MCP Server
npm install -g youtube-scrap-mcp{
"mcpServers": {
"youtube-scrap": {
"command": "npx",
"args": ["youtube-scrap-mcp"]
}
}
}git clone https://github.com/your-username/youtube-scrap-mcp.git
cd youtube-scrap-mcp
pnpm install
pnpm build{
"mcpServers": {
"youtube-scrap": {
"command": "node",
"args": ["/path/to/youtube-scrap-mcp/dist/stdio.js"]
}
}
}Extract comprehensive content from YouTube videos including title, description, and transcript. Automatically uses available subtitles with language priority or falls back to optimized Whisper speech-to-text conversion with language detection.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | YouTube video URL to extract content from | |
| includeTitle | No | Include video title in the output | |
| includeDescription | No | Include video description in the output | |
| includeTranscript | No | Include video transcript/audio content in the output |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable context beyond annotations by explaining the extraction process: using available subtitles with language priority and falling back to Whisper speech-to-text with language detection. Annotations cover read-only, idempotent, and non-destructive hints, so the description appropriately supplements with operational details without contradiction.
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 concise and front-loaded, efficiently conveying the tool's purpose and key behavioral traits in two sentences. Every sentence adds value: the first states what is extracted, and the second explains the extraction method, with no wasted words.
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 tool's complexity (extraction with fallback mechanisms), annotations provide safety hints, and schema covers parameters well, the description is largely complete. However, without an output schema, it could benefit from mentioning the output format or structure, though it hints at content types. This minor gap prevents a perfect score.
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 description does not need to detail parameters. It mentions content types like title, description, and transcript, which align with the boolean parameters in the schema, but adds no extra semantic meaning beyond what the schema provides. This meets the baseline for high schema coverage.
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 verb 'extract' and the resource 'YouTube videos', specifying the comprehensive content types: title, description, and transcript. It distinguishes this tool's functionality by detailing the extraction process with subtitle priority and Whisper fallback, making the purpose specific and unambiguous.
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 implies usage for extracting content from YouTube videos, but it does not provide explicit guidance on when to use this tool versus alternatives, such as tools for other video platforms or different extraction methods. Since there are no sibling tools, this is adequate but lacks broader context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1extract-youtubeScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'extract-youtube' follows a clear verb-noun pattern.
One tool is too few for a server named 'YouTube Content Extractor MCP', which suggests a broader scope. The tool only extracts content, lacking operations like search, list, or manage videos, making the set feel incomplete and thin for the domain.
The server is severely incomplete for YouTube content extraction. It only provides extraction, with no tools for discovering videos (e.g., search, list channels), managing data, or handling metadata updates, creating significant gaps in typical workflows.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
An MCP server that gives any LLM or agent clean YouTube transcripts on demand: a single video, a whole channel, or a playlist, plus AI cleanup of auto-generated captions. API-key auth, credit-based, same backend as the public v1 API. Get a free API key with 25 free credits at youtubetranscriptdownload.com/account.
MCP server for RiverScript, an AI transcription platform - fetches transcripts shared via a link.
Any social-video URL → transcript, metadata, frames, OCR, summary, search, Q&A. MCP server + x402.
Fetch transcripts, subtitles, chapters, metadata and frames from YouTube and 10+ video platforms
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