YouTube MCP Server Enhanced 🚀

A comprehensive Micro-Conversational Processor (MCP) server for extracting and analyzing YouTube data using yt-dlp.

🚀 Features

Core Extraction

• Video Information: Metadata, statistics, engagement metrics
• Channel Information: Stats, subscriber count, view count, verification status
• Playlist Details: Video lists, durations, total views
• Comments: Threaded comments with replies and engagement
• Transcripts: Auto-generated and manual subtitles

Advanced Capabilities

• YouTube Search: Search for videos, channels, and playlists
• Trending Videos: Get trending content by region
• Batch Processing: Extract from multiple URLs concurrently
• Intelligent Caching: Configurable TTL-based caching
• Automatic Retries: Exponential backoff for failed requests
• Health Monitoring: Real-time extractor status and configuration

🛠️ Installation

Prerequisites

• Python 3.10+
• uv package manager (required)
• yt-dlp (automatically installed via uv)

⚠️ Important: This project requires uv to run properly. Install it first:

# Install uv (macOS/Linux)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or via Homebrew (macOS)
brew install uv

# Or via pip
pip install uv

Setup

# Clone the repository
git clone <repository-url>
cd youtube-mcp-server-enhanced

# Install yt-dlp and all dependencies
uv add yt-dlp
uv sync

# Verify installation
uv run yt-dlp --version

⚙️ Configuration

Environment Variables (.env file)

Create a .env file in the project root to configure the server:

# Copy the example file
cp .env.example .env

# Edit with your preferred settings
nano .env

Example .env configuration:

# Rate limiting (e.g., "500K" for 500KB/s, "1M" for 1MB/s)
YOUTUBE_RATE_LIMIT=500K

# Retry configuration
YOUTUBE_MAX_RETRIES=5
YOUTUBE_RETRY_DELAY=2.0
YOUTUBE_TIMEOUT=600

# Caching
YOUTUBE_ENABLE_CACHE=true
YOUTUBE_CACHE_TTL=3600

# Logging level
LOG_LEVEL=INFO

MCP Client Configuration

Claude Desktop (macOS)

Add to your ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "youtube-mcp-server": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/youtube-mcp-server-enhanced",
        "python",
        "-m",
        "src.youtube_mcp_server.server"
      ],
      "env": {
        "YOUTUBE_RATE_LIMIT": "500K",
        "YOUTUBE_MAX_RETRIES": "5",
        "YOUTUBE_RETRY_DELAY": "2.0",
        "YOUTUBE_TIMEOUT": "600",
        "YOUTUBE_ENABLE_CACHE": "true",
        "YOUTUBE_CACHE_TTL": "3600"
      }
    }
  }
}

Other MCP Clients

For other MCP clients, configure the server command as:

uv run --directory /path/to/youtube-mcp-server-enhanced python -m src.youtube_mcp_server.server

Default Values

• Rate Limit: None (uses YouTube's default)
• Max Retries: 5 (increased from 3 for better reliability)
• Retry Delay: 2.0 seconds (with exponential backoff)
• Timeout: 600 seconds (10 minutes)
• Cache TTL: 3600 seconds (1 hour)
• Cache: Enabled by default

🎯 Available MCP Tools

Data Extraction

Search & Discovery

Analysis & Insights

Batch Operations

System Management

MCP Prompts

📊 Data Models

VideoInfo

{
    "metadata": {
        "id": "video_id",
        "title": "Video Title",
        "description": "Video description...",
        "uploader": "Channel Name",
        "uploader_id": "channel_id",
        "upload_date": "20240101",
        "tags": ["tag1", "tag2"],
        "categories": ["Entertainment"],
        "thumbnail": "https://..."
    },
    "statistics": {
        "view_count": 1000,
        "like_count": 50,
        "comment_count": 25,
        "duration_seconds": 120,
        "duration_string": "2:00"
    },
    "engagement": {
        "like_to_view_ratio": 0.05,
        "comment_to_view_ratio": 0.025,
        "like_rate_percentage": "5.000%",
        "comment_rate_percentage": "2.500%"
    },
    "technical": {
        "age_limit": 0,
        "availability": "public",
        "live_status": "not_live"
    }
}

ChannelInfo

{
    "id": "channel_id",
    "name": "Channel Name",
    "url": "https://youtube.com/@channel",
    "description": "Channel description...",
    "avatar_url": "https://...",
    "banner_url": "https://...",
    "verified": true,
    "country": "US",
    "language": "en",
    "tags": ["tag1", "tag2"],
    "statistics": {
        "subscriber_count": 10000,
        "video_count": 150,
        "view_count": 500000
    }
}

PlaylistInfo

{
    "id": "playlist_id",
    "title": "Playlist Title",
    "description": "Playlist description...",
    "uploader": "Channel Name",
    "uploader_id": "channel_id",
    "video_count": 25,
    "total_duration_seconds": 7200,
    "total_duration_formatted": "2h 0m",
    "total_views": 50000,
    "videos": [
        {
            "video_id": "video_id",
            "title": "Video Title",
            "uploader": "Channel Name",
            "duration": 300,
            "view_count": 2000,
            "playlist_index": 1
        }
    ]
}

🔍 Usage Examples

Basic Video Analysis

# Get comprehensive video information
video_info = await get_video_info("https://www.youtube.com/watch?v=dQw4w9WgXcQ")

# Extract video comments
comments = await get_video_comments("https://www.youtube.com/watch?v=dQw4w9WgXcQ", max_comments=50)

# Get video transcript
transcript = await get_video_transcript("https://www.youtube.com/watch?v=dQw4w9WgXcQ")

# Search within transcript
results = await search_transcript("https://www.youtube.com/watch?v=dQw4w9WgXcQ", "never gonna")

Channel and Playlist Analysis

# Get channel information
channel_info = await get_channel_info("https://www.youtube.com/@RickAstleyYT")

# Get playlist details
playlist_info = await get_playlist_info("https://www.youtube.com/playlist?list=...")

Search and Discovery

# Search for videos
results = await search_youtube("Python programming tutorials", "video", 10)

# Get trending videos
trending = await get_trending_videos("US", 20)

Advanced Analysis

# Analyze video engagement with benchmarks
engagement = await analyze_video_engagement("https://www.youtube.com/watch?v=dQw4w9WgXcQ")

# Compare multiple videos
comparison = await compare_videos([
    "https://youtube.com/watch?v=video1",
    "https://youtube.com/watch?v=video2"
])

Batch Processing

# Process multiple URLs concurrently
results = await batch_extract_urls([
    "https://youtube.com/watch?v=video1",
    "https://youtube.com/watch?v=video2"
], "video")

⚡ Performance Features

Caching

• In-Memory Cache: Configurable TTL-based caching
• Cache Keys: Unique keys for each request type and parameters
• Cache Management: View stats, clear cache, configure TTL

Retry Logic

• Automatic Retries: Configurable retry attempts
• Exponential Backoff: Increasing delay between retries
• Error Handling: Graceful degradation on failures

Batch Processing

• Concurrent Extraction: Process multiple URLs simultaneously using asyncio
• Async Operations: Non-blocking I/O for better performance
• Result Aggregation: Combined results with success/failure counts

🏥 Health Monitoring

Health Status

health = await get_extractor_health()
# Returns:
{
    "health": {
        "status": "healthy",
        "yt_dlp_available": true,
        "yt_dlp_version": "2025.6.30",
        "cache": {"enabled": true, "size": 5, "ttl": 3600},
        "config": {"rate_limit": "1M", "max_retries": 3, "timeout": 300}
    },
    "cache": {
        "enabled": true,
        "size": 5,
        "ttl": 3600,
        "keys": ["key1", "key2"],
        "total_keys": 5
    },
    "server_version": "0.1.0",
    "mcp_version": "1.0.0"
}

Configuration View

config = await get_extractor_config()
# Returns current extractor settings and status

🚨 Error Handling

Retry Strategy

• Automatic Retries: Up to 5 attempts by default (configurable)
• Exponential Backoff: 2s, 4s, 8s delays
• Rate Limiting: 500KB/s limit with 2-second sleep intervals
• Graceful Degradation: Return partial results when possible

Error Types

• YouTubeExtractorError: Extraction-specific errors
• InvalidURLError: Invalid YouTube URL format
• RuntimeError: General execution errors

Troubleshooting

Rate Limiting Issues

If you encounter rate limiting:

1. Increase sleep intervals in .env: YOUTUBE_RETRY_DELAY=3.0
2. Lower rate limit: YOUTUBE_RATE_LIMIT=300K
3. Reduce concurrent requests

yt-dlp Not Working

1. Ensure uv is installed: uv --version
2. Verify yt-dlp installation: uv run yt-dlp --version
3. The server automatically uses uv run yt-dlp if direct access fails

MCP Connection Issues

1. Restart your MCP client after code changes
2. Check logs for specific error messages
3. Verify environment variables are loaded correctly

🔧 Development

Running the Server

⚠️ Always use uv run to ensure proper dependency management:

# Start the MCP server (recommended)
uv run python -m src.youtube_mcp_server.server

# Or if you have a run_server.py file
uv run python run_server.py

Testing

# Run all tests
uv run pytest tests/

# Run specific test file
uv run pytest tests/test_basic.py

# Run with coverage
uv run pytest --cov=src tests/

📈 Use Cases

Content Analysis

• Video Performance: Analyze view counts, engagement metrics
• Channel Growth: Track subscriber and view count trends
• Content Discovery: Find trending and popular content

Research & Analytics

• Market Research: Analyze competitor channels and content
• Trend Analysis: Identify trending topics and content types
• Audience Insights: Understand viewer preferences and behavior

Content Management

• Playlist Organization: Manage and analyze video collections
• Comment Moderation: Extract and analyze user feedback
• Transcript Analysis: Process and search video content

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

📄 License

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

🙏 Acknowledgments

• yt-dlp: The core YouTube extraction engine
• FastMCP: The MCP server framework
• Pydantic: Data validation and serialization

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: info@labeveryday.com

🗺️ Roadmap

• Batch processing for multiple videos
• Caching layer for improved performance
• Advanced analytics (engagement analysis, benchmarks)
• Rate limiting and quota management
• Export functionality (JSON, CSV, etc.)
• 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.