YouTube Knowledge MCP

YouTube Knowledge MCP

A Model Context Protocol (MCP) server that gives AI assistants the ability to search, analyze, and extract knowledge from YouTube videos.

Category
Visit Server

README

YouTube Knowledge MCP

npm version License: MIT Node.js GitHub stars

A Model Context Protocol (MCP) server that gives AI assistants the ability to search, analyze, and extract knowledge from YouTube videos. Works with Claude Desktop, Claude Code, Claude.ai, Cursor and any MCP-compatible client.

Supports both local (stdio) and remote (Streamable HTTP) transports.

YouTube Knowledge MCP

Features

  • Search videos by keyword or phrase
  • Fetch videos from playlists or channels
  • Get video info (title, channel, duration, views, likes, comments, description, tags)
  • Extract transcripts (auto-generated or manual captions)
  • Get chapters (timestamps and structure)
  • Get comments (top comments sorted by popularity)
  • Get channel info (name, handle, subscriber count, description)
  • List formats (available download resolutions and codecs)
  • Download videos with quality selection (local mode only)
  • Save to library (summaries, notes, skills) (local mode only)
  • List library with tag filtering (local mode only)

Prerequisites

Installation

Via npm (Recommended)

npm install -g youtube-knowledge-mcp

Via npx (no installation)

Configure directly with npx (see Configuration section).

From source

git clone https://github.com/teobouancheau/youtube-knowledge-mcp.git
cd youtube-knowledge-mcp
npm install
npm run build

Configuration

Local (stdio) -- Claude Desktop, Claude Code, Cursor

Quick Start with npx

{
  "mcpServers": {
    "youtube-knowledge": {
      "command": "npx",
      "args": ["-y", "youtube-knowledge-mcp"]
    }
  }
}

With Global Installation

npm install -g youtube-knowledge-mcp
{
  "mcpServers": {
    "youtube-knowledge": {
      "command": "youtube-knowledge-mcp"
    }
  }
}

Configuration File Locations

Client Path
Claude Desktop (macOS) ~/Library/Application Support/Claude/claude_desktop_config.json
Claude Desktop (Windows) %APPDATA%\Claude\claude_desktop_config.json
Claude Desktop (Linux) ~/.config/Claude/claude_desktop_config.json
Claude Code .mcp.json in your project or ~/.claude/settings.json
Cursor .cursor/mcp.json in your project

Restart your client after updating configuration.

Remote (HTTP) -- Claude.ai, Claude Mobile, Custom Connectors

The server supports Streamable HTTP transport for remote access via Claude's official connectors.

Self-hosted

npm run build
npm run start:http

The server listens on PORT (default 3000). Set PORT environment variable to change.

Docker

npm run build
docker build -t youtube-knowledge-mcp .
docker run -p 3000:10000 youtube-knowledge-mcp

Deploy to Render

  1. Push to GitHub
  2. Create a Web Service on Render with Docker runtime
  3. Render sets PORT automatically
  4. Add the Render URL as a custom connector in Claude.ai > Settings > Connectors

Connect via Claude.ai

  1. Go to Settings > Connectors
  2. Click Add custom connector
  3. Enter your server URL (e.g., https://your-app.onrender.com/mcp)
  4. Click Add

MCP Tools

Remote + Local (10 tools)

search_videos

Search YouTube for videos by keyword or phrase.

Parameter Type Default Description
query string required Search query
limit number 5 Maximum results to return (1-20)

Returns: video IDs, titles, durations, channels, view counts, URLs

fetch_videos

List videos from a YouTube playlist or channel.

Parameter Type Default Description
url string required Playlist URL, channel URL, or handle
limit number 20 Maximum videos to return (1-100)

get_video_info

Get detailed metadata for a single YouTube video.

Parameter Type Description
video string Video ID or URL

Returns: title, channel, duration, upload date, view count, like count, comment count, description, tags, thumbnail URL

get_transcript

Extract the full transcript from a YouTube video.

Parameter Type Default Description
video string required Video ID or URL
language string "en" Preferred language (ISO 639-1)

Returns: plain text transcript with word count and detected language

get_chapters

Extract chapter markers and timestamps from a YouTube video.

Parameter Type Description
video string Video ID or URL

Returns: chapter titles with start and end times. Empty list if no chapters found.

get_comments

Get top comments from a YouTube video sorted by popularity.

Parameter Type Default Description
video string required Video ID or URL
limit number 20 Maximum top-level comments to return (1-50)

Returns: author, text, like count, pinned status

get_channel_info

Get metadata for a YouTube channel.

Parameter Type Description
channel string Channel URL, handle (e.g., @Fireship), or name

Returns: channel name, handle, subscriber count, description, channel URL

search_channels

Search YouTube for channels by keyword or phrase.

Parameter Type Default Description
query string required Search query for channels
limit number 5 Maximum channels to return (1-20)

Returns: channel names, handles, subscriber counts, descriptions, URLs

get_playlist_info

Get metadata for a YouTube playlist.

Parameter Type Description
url string YouTube playlist URL

Returns: title, channel, video count, last updated date, description

list_formats

List all available download formats for a YouTube video.

Parameter Type Description
video string Video ID or URL

Returns: format IDs, extensions, resolutions, FPS, codecs, file sizes

Local Only (3 tools, stdio mode)

These tools operate on the local filesystem and are only available in stdio mode.

download_video

Download a YouTube video to local disk.

Parameter Type Default Description
video string required Video ID or URL
quality string "best" Quality preset (best, 2160p, 1440p, 1080p, 720p, 480p, 360p, audio)
formatId string - Specific format code from list_formats
outputDir string - Custom output directory

save_to_library

Save a summary or skill note to your local YouTube knowledge library.

Parameter Type Description
videoId string YouTube video ID
title string Video title
content string Content to save (markdown format)
contentType string "summary" or "skill"
tags string[] Tags for categorization (optional)
channel string Channel name (optional)

list_library

List all saved items in your local knowledge library.

Parameter Type Description
tag string Filter by tag (optional, partial match)

Library Storage

Content is stored in ~/.youtube-knowledge/:

~/.youtube-knowledge/
├── transcripts/          # Cached transcripts
│   └── {video_id}.txt
├── library/              # Saved content
│   └── {video_id}/
│       ├── metadata.json
│       ├── summary.md
│       └── skill.md
├── downloads/            # Downloaded videos
│   └── {video_title}.{ext}
└── index.json            # Searchable index

Usage Examples

Search and analyze

"Search YouTube for 'transformer architecture explained' and summarize the top result"

Explore a channel

"Show me the latest videos from @ThePrimeagen and get the chapters for the most recent one"

Deep content analysis

"Get the transcript and chapters for this video, then create a structured summary"

Audience sentiment

"What are the top comments on this video? What does the audience think?"

Download a video (local only)

"Download this video in 1080p: https://youtube.com/watch?v=ABC123"

Testing

npm test              # Run all tests
npm run test:watch    # Watch mode
npm run test:coverage # Coverage report

Development

npm run dev        # Watch mode
npm run build      # Build for production
npm run rebuild    # Clean and rebuild
npm start          # Run server (stdio)
npm run start:http # Run server (HTTP)
npm run validate   # Typecheck + lint + test

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Run tests (npm run validate)
  4. Submit a pull request

License

MIT License - see LICENSE for details.

Acknowledgments


<div align="center"> <strong>Built by <a href="https://github.com/teobouancheau">teobouancheau</a></strong> <br> <sub>AI + YouTube knowledge to supercharge content creation</sub> </div>

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured