MCP YouTube Extract

A Model Context Protocol (MCP) server for YouTube operations, demonstrating core MCP concepts including tools and logging.

Features

• MCP Server: A fully functional MCP server with:
• Tools: Extract information from YouTube videos including metadata and transcripts
• Comprehensive Logging: Detailed logging throughout the application
• Error Handling: Robust error handling with fallback logic for transcripts
• YouTube Integration: Built-in YouTube API capabilities:
• Extract video information (title, description, channel, publish date)
• Get video transcripts with intelligent fallback logic
• Support for both manually created and auto-generated transcripts

📦 Available on PyPI

This package is now available on PyPI! You can install it directly with:

pip install mcp-youtube-extract

Visit the package page: mcp-youtube-extract on PyPI

Installation# Quick Start (Recommended)

The easiest way to get started is to install from PyPI:

pip install mcp-youtube-extract

Or using pipx (recommended for command-line tools):

pipx install mcp-youtube-extract

This will install the latest version with all dependencies. You can then run the MCP server directly:

mcp_youtube_extract

Using uv (Development)

For development or if you prefer uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and install the project
git clone https://github.com/sinjab/mcp_youtube_extract.git
cd mcp_youtube_extract

# Install dependencies (including dev dependencies)
uv sync --dev

# Set up your API key for development
cp .env.example .env\n\n# Edit .env and add your YouTube API key

From source

1. Clone the repository:

   git clone https://github.com/sinjab/mcp_youtube_extract.git
   cd mcp_youtube_extract
   

2. Install in development mode:

   uv sync --dev
   

Configuration# Environment Variables

For development, create a .env file in the project root with your YouTube API key:

YOUTUBE_API_KEY=your_youtube_api_key_here

For production, set the environment variable directly in your system:

export YOUTUBE_API_KEY=your_youtube_api_key_here

Required:

• YOUTUBE_API_KEY: Your YouTube Data API key (required for video metadata)

Getting Your YouTube API Key

To use this MCP server, you'll need a YouTube Data API key. Here's how to get one:

Step 1: Create a Google Cloud Project

1. Go to the Google Cloud Console
2. Click "Select a project" at the top of the page
3. Click "New Project" and give it a name (e.g., "MCP YouTube Extract")
4. Click "Create"

Step 2: Enable the YouTube Data API

1. In your new project, go to the API Library
2. Search for "YouTube Data API v3"
3. Click on it and then click "Enable"

Step 3: Create API Credentials

1. Go to the Credentials page
2. Click "Create Credentials" and select "API Key"
3. Your new API key will be displayed - copy it immediately
4. Click "Restrict Key" to secure it (recommended)

Step 4: Restrict Your API Key (Recommended)

1. In the API key settings, click "Restrict Key"
2. Under "API restrictions", select "Restrict key"
3. Choose "YouTube Data API v3" from the dropdown
4. Click "Save"

Step 5: Set Up Billing (Required)

1. Go to the Billing page
2. Link a billing account to your project
3. Note: YouTube Data API has a free tier of 10,000 units per day, which is typically sufficient for most use cases

API Key Usage Limits

• Free Tier: 10,000 units per day
• Cost: $5 per 1,000 units after free tier
• Typical Usage:
• Getting video info: ~1 unit per request
• Getting transcripts: ~1 unit per request
• Most users stay well within the free tier

Security Best Practices

• Never commit your API key to version control
• Use environment variables as shown in the configuration section
• Restrict your API key to only the YouTube Data API
• Monitor usage in the Google Cloud Console

Usage# Running the MCP Server

Using PyPI Installation (Recommended)

pip install mcp-youtube-extract

# Run the server
mcp_youtube_extract

Using Development Setup

uv run mcp_youtube_extract

# Or directly
python -m mcp_youtube_extract.server

Running Tests

uv run pytest

# Run specific pytest test
uv run pytest tests/test_with_api_key.py

# Run tests with coverage
uv run pytest --cov=src/mcp_youtube_extract --cov-report=term-missing

Note: The tests/ directory contains 4 files:

• test_context_fix.py - Pytest test for context API fallback functionality
• test_with_api_key.py - Pytest test for full functionality with API key
• test_youtube_unit.py - Unit tests for core YouTube functionality
• test_inspector.py - Standalone inspection script (not a pytest test)

Test Coverage: The project currently has 62% overall coverage with excellent coverage of core functionality:

• youtube.py: 81% coverage (core business logic)
• logger.py: 73% coverage (logging utilities)
• server.py: 22% coverage (MCP protocol handling)
• __init__.py: 100% coverage (package initialization)

Running the Inspection Script

The test_inspector.py file is a standalone script that connects to the MCP server and validates its functionality:

uv run python tests/test_inspector.py

This script will:

• Connect to the MCP server
• List available tools, resources, and prompts
• Test the get_yt_video_info tool with a sample video
• Validate that the server is working correctly

Using the YouTube Tool

The server provides one main tool: get_yt_video_info

This tool takes a YouTube video ID and returns:

• Video metadata (title, description, channel, publish date)
• Video transcript (with fallback logic for different transcript types)

Example Usage:

video_id = "dQw4w9WgXcQ"
result = get_yt_video_info(video_id)

Client Configuration

To use this MCP server with a client, add the following configuration to your client's settings:

Using PyPI Installation (Recommended)

{
  "mcpServers": {
    "mcp_youtube_extract": {
      "command": "mcp_youtube_extract",
      "env": {
        "YOUTUBE_API_KEY": "your_youtube_api_key"
      }
    }
  }
}

Using Development Setup

{
  "mcpServers": {
    "mcp_youtube_extract": {
      "command": "uv",
      "args": [
        "--directory",
        "<your-project-directory>",
        "run",
        "mcp_youtube_extract"
      ],
      "env": {
        "YOUTUBE_API_KEY": "your_youtube_api_key"
      }
    }
  }
}

Development# Project Structure

mcp_youtube_extract/
├── src/
│   └── mcp_youtube_extract/
│       ├── __init__.py
│       ├── server.py          # MCP server implementation
│       ├── youtube.py         # YouTube API utilities
│       └── logger.py          # Logging configuration
├── tests/
│   ├── __init__.py
│   ├── test_context_fix.py    # Context API fallback tests
│   ├── test_inspector.py      # Server inspection tests
│   ├── test_with_api_key.py   # Full functionality tests
│   └── test_youtube_unit.py   # Unit tests for core functionality
├── logs/                      # Application logs
├── .env                       # Environment variables (create from .env.example)
├── .gitignore                 # Git ignore rules (includes coverage files)
├── pyproject.toml
├── LICENSE                    # MIT License
└── README.md

Testing Strategy

The project uses a comprehensive testing approach:

1. Unit Tests (test_youtube_unit.py): Test core YouTube functionality with mocked APIs
2. Integration Tests (test_context_fix.py, test_with_api_key.py): Test full server functionality
3. Manual Validation (test_inspector.py): Interactive server inspection tool

Error Handling

The project includes robust error handling:

• Graceful API failures: Returns appropriate error messages instead of crashing
• Fallback logic: Multiple strategies for transcript retrieval
• Consistent error responses: Standardized error message format
• Comprehensive logging: Detailed logs for debugging and monitoring

Building

uv add --dev hatch

# Build the package
uv run hatch build

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Getting Started

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Support

If you encounter any issues or have questions, please:

1. Check the existing issues
2. Create a new issue with detailed information about your problem
3. Include logs and error messages when applicable