labeveryday/youtube-mcp-server-enhanced

YouTube MCP Server Enhanced 🚀

A comprehensive Model Context Protocol (MCP) server for YouTube data extraction and analysis. Extract video metadata, comments, transcripts, channel information, and perform engagement analysis - all without requiring API keys!

✨ Features

🎥 Video Analysis

• Comprehensive metadata extraction (title, description, tags, statistics)
• Engagement metrics analysis (like rates, comment rates, benchmarking)
• Upload performance tracking and daily view averages

💬 Comment Extraction

• Threaded comments with replies
• Configurable limits to control extraction volume
• Author information and engagement metrics per comment

📝 Transcript Processing

• Auto-generated and manual subtitle extraction
• Timestamped entries with precise timing
• Full-text search within transcripts
• Text-at-timestamp lookup functionality

📺 Channel & Playlist Analysis

• Channel statistics and metadata
• Playlist analysis with total duration and view counts
• Video listing with individual metrics

🔧 Developer-Friendly

• Type-safe with Pydantic models
• Async/await support for better performance
• Comprehensive error handling
• Extensible architecture

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/labeveryday/youtube-mcp-server-enhanced.git
cd youtube-mcp-server-enhanced

# Install dependencies
pip install -e .

# Or install in development mode
pip install -e ".[dev]"

Running as MCP Server

# Start the MCP server
uv run run_server.py

Testing with MCP Inspector

You can test the server using the MCP Inspector tool:

# Test with uv (recommended)
npx @modelcontextprotocol/inspector uv --directory ./ "run" "run_server.py"

# Or test with direct Python execution
npx @modelcontextprotocol/inspector python run_server.py

The MCP Inspector provides a web interface to:

• View available tools and their schemas
• Test tool execution with sample inputs
• Debug server responses and error handling
• Validate MCP protocol compliance

Using with MCP Clients

Integration with Amazon Q CLI

To integrate this MCP server with Amazon Q CLI, configure the MCP server in your AWS configuration:

Create or update the file at ~/.aws/amazonq/mcp.json:

{
  "mcpServers": {
    "youtube-enhanced": {
      "command": "uv",
      "args": ["--directory", "/path/to/youtube-mcp-server-enhanced", "run", "run_server.py"],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

Replace /path/to/youtube-mcp-server-enhanced with the actual path to your repository.

Other MCP Clients

For other MCP clients, use the standard configuration:

{
  "mcpServers": {
    "youtube-enhanced": {
      "command": "uv",
      "args": ["run", "run_server.py"],
      "cwd": "/path/to/youtube-mcp-server-enhanced"
    }
  }
}

🛠️ Available Tools

1. get_video_info

Extract comprehensive video information including metadata and statistics.

Parameters:

• url (string, required): YouTube video URL

Example:

{
  "name": "get_video_info",
  "arguments": {
    "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  }
}

2. get_video_comments

Extract comments from a YouTube video with optional limits.

Parameters:

• url (string, required): YouTube video URL
• max_comments (integer, optional): Maximum comments to extract (default: 100)

3. get_video_transcript

Extract transcript/subtitles from a YouTube video.

Parameters:

• url (string, required): YouTube video URL

4. analyze_video_engagement

Analyze engagement metrics with industry benchmarks.

Parameters:

• url (string, required): YouTube video URL

5. search_transcript

Search for specific text within a video's transcript.

Parameters:

• url (string, required): YouTube video URL
• query (string, required): Text to search for
• case_sensitive (boolean, optional): Case-sensitive search (default: false)

6. get_channel_info

Extract information about a YouTube channel.

Parameters:

• url (string, required): YouTube channel URL

7. get_playlist_info

Extract information about a YouTube playlist.

Parameters:

• url (string, required): YouTube playlist URL

📊 Example Outputs

Video Information

# Video Information

## Basic Details
- **Title**: Rick Astley - Never Gonna Give You Up (Official Video)
- **Channel**: Rick Astley
- **Duration**: 3:33
- **Upload Date**: Oct 25, 2009

## Statistics
- **Views**: 1,670,334,246
- **Likes**: 18,439,766 (1.104% of views)
- **Comments**: 2,300,000 (0.138% of views)

## Engagement Analysis
- **Like Rate**: Excellent (above 1%)
- **Comment Rate**: Good (above 0.1%)

Transcript Search

# Transcript Search Results

## Query: "never gonna"
- **Matches Found**: 8

## Results
**1. 00:48**: Never gonna give you up
**2. 01:02**: Never gonna let you down
**3. 01:15**: Never gonna run around and desert you

🏗️ Architecture

youtube-mcp-server-enhanced/
├── src/youtube_mcp_server/
│   ├── models/           # Pydantic data models
│   │   ├── video.py      # Video-related models
│   │   ├── comment.py    # Comment models
│   │   ├── transcript.py # Transcript models
│   │   ├── channel.py    # Channel models
│   │   └── playlist.py   # Playlist models
│   ├── extractors/       # Data extraction logic
│   │   └── youtube_extractor.py
│   ├── utils/           # Utility functions
│   │   ├── url_utils.py  # URL parsing utilities
│   │   ├── format_utils.py # Formatting helpers
│   │   └── errors.py     # Custom exceptions
│   └── server.py        # MCP server implementation
├── tests/               # Test suite
├── examples/            # Usage examples
└── run_server.py        # Server entry point

🔧 Development

Setting up Development Environment

# Clone and install in development mode
git clone https://github.com/labeveryday/youtube-mcp-server-enhanced.git
cd youtube-mcp-server-enhanced
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Format code
black src/ tests/
isort src/ tests/

# Type checking
mypy src/

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=youtube_mcp_server

# Run specific test file
pytest tests/test_extractor.py

📋 Requirements

• Python 3.10+
• yt-dlp - YouTube data extraction
• mcp - Model Context Protocol support
• pydantic - Data validation and serialization
• asyncio-throttle - Rate limiting support

🤝 Contributing

We welcome contributions! Please see our for details.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass (pytest)
6. Format your code (black, isort)
7. Commit your changes (git commit -m 'Add amazing feature')
8. Push to the branch (git push origin feature/amazing-feature)
9. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• yt-dlp team for the excellent YouTube extraction library
• MCP team for the Model Context Protocol specification
• Pydantic team for the fantastic data validation library

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email:

🗺️ Roadmap

• Batch processing for multiple videos
• Caching layer for improved performance
• Export functionality (JSON, CSV, etc.)
• Advanced analytics (sentiment analysis, trending topics)
• Rate limiting and quota management
• WebSocket support for real-time updates
• Integration examples with popular MCP clients

Made with ❤️ by Du'An Lightfoot

Empowering developers to extract meaningful insights from YouTube content through the Model Context Protocol.