youtube-gemini-mcp 0.1.1
Session-based conversational YouTube video analysis MCP server using Gemini 2.5 Pro
Requires Python: <4.0,>=3.10
Keywords: youtube, gemini, mcp, video-analysis, ai, claude, conversational

![YouTube Gemini MCP Hero](https://raw.githubusercontent.com/aigentive/youtube-gemini-mcp/main/assets/hero-image.png)

# YouTube Gemini MCP Server

A Model Context Protocol (MCP) server that provides conversational YouTube video analysis capabilities using Gemini 2.5 Pro. Analyze videos through multi-turn conversations with advanced visual understanding.

## 🎯 What Problems Does This Solve?

### Traditional Video Analysis Pain Points
❌ **Transcript-only limitations** - Missing visual context and scene understanding  
❌ **No conversation memory** - Each analysis starts from scratch  
❌ **Manual workflows** - Complex download/upload processes  

### Our Solution  
✅ **Full video analysis** - Visual, audio, and contextual understanding  
✅ **Session memory** - Builds on previous analysis automatically  
✅ **Integrated workflows** - YouTube URL to analysis in one step  

## 🚀 Key Capabilities

### 🔄 Session-Based Video Conversations
Create persistent sessions for multi-turn video analysis:
```python
# Create session
create_video_session(
    description="Analyze machine learning lecture",
    video_source="https://youtube.com/watch?v=abc123",
    source_type="youtube_url"
)

# Continue conversation
analyze_video_in_session(session_id="uuid", prompt="What are the key concepts?")
analyze_video_in_session(session_id="uuid", prompt="Explain the neural networks part")
```

### 📹 Direct YouTube Processing
No downloads required - process YouTube videos directly:
```python
analyze_youtube_video(
    youtube_url="https://youtube.com/watch?v=abc123",
    prompt="Summarize this video's main points"
)
```

### 💾 Local Video Support
Upload and analyze local video files (48-hour retention):
```python
analyze_local_video(
    video_path="/path/to/video.mp4",
    prompt="What happens in this video?"
)
```

## 🛠️ Installation

### Prerequisites
- Python 3.10+
- Google AI API key
- Poetry (recommended) or pip

### Setup
```bash
# Clone repository
git clone https://github.com/aigentive/youtube-gemini-mcp
cd youtube-gemini-mcp

# Install with Poetry
poetry install

# Or with pip
pip install -e .

# Set environment variable
export GOOGLE_API_KEY="your_google_api_key_here"
```

## 🔧 Claude Desktop Integration

### Quick Setup

1. **Get Google AI API Key**: Visit [Google AI Studio](https://ai.google.dev/) to get your free API key
2. **Add to Claude Desktop**: Copy the configuration below to your Claude Desktop settings

### Production Configuration

Add to your Claude Desktop MCP configuration (`claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "youtube-gemini-mcp": {
      "command": "youtube-gemini-mcp",
      "env": {
        "GOOGLE_API_KEY": "your_google_api_key_here"
      }
    }
  }
}
```

### Development Configuration

For development work, use the Poetry configuration:

```json
{
  "mcpServers": {
    "youtube-gemini-mcp-dev": {
      "command": "poetry",
      "args": ["run", "python", "-m", "youtube_gemini_mcp.server"],
      "cwd": "/absolute/path/to/youtube-gemini-mcp",
      "env": {
        "GOOGLE_API_KEY": "your_google_api_key_here",
        "LOG_LEVEL": "DEBUG"
      }
    }
  }
}
```

### Configuration Templates

We provide ready-to-use configuration files:
- **Development**: `mcp-config.poetry.json` - For local development
- **Production**: `mcp-config.private.json.example` - Copy and customize

## 📚 Available Tools

| Tool | Description |
|------|-------------|
| `create_video_session` | Create new conversational analysis session |
| `analyze_video_in_session` | Analyze video within session context |
| `analyze_youtube_video` | Single-shot YouTube video analysis |
| `analyze_local_video` | Single-shot local video analysis |
| `get_session_status` | Get session information and history |
| `list_active_sessions` | List all active sessions |
| `close_session` | Close session and cleanup resources |
| `validate_youtube_url` | Validate and normalize YouTube URLs |
| `get_usage_guide` | Comprehensive documentation |
| `get_server_stats` | Server health and statistics |

## 🎯 Use Cases

### Educational Content Analysis
Analyze lectures, tutorials, and educational videos with follow-up questions.

### Content Research
Research documentaries, interviews, and informational content systematically.

### Video Summarization
Extract key insights and create summaries from long-form content.

### Training Material Development
Analyze existing training videos to extract learning objectives and key points.

## ⚡ Quick Examples

### Analyze a YouTube Video
```python
# Direct analysis - perfect for quick insights
analyze_youtube_video(
    youtube_url="https://youtube.com/watch?v=dQw4w9WgXcQ",
    prompt="What is this video about? Provide a comprehensive summary."
)
```

### Session-Based Analysis
```python
# Create persistent session for multi-turn conversation
session = create_video_session(
    description="Learning about machine learning fundamentals",
    video_source="https://youtube.com/watch?v=abc123",
    session_name="ML Fundamentals Study"
)

# Build understanding through conversation
analyze_video_in_session(
    session_id=session["session_id"], 
    prompt="What are the main topics covered in this lecture?"
)

analyze_video_in_session(
    session_id=session["session_id"], 
    prompt="Focus on the neural networks section - how are they explained?"
)

analyze_video_in_session(
    session_id=session["session_id"], 
    prompt="What practical examples or demonstrations are shown?"
)

analyze_video_in_session(
    session_id=session["session_id"], 
    prompt="Based on our discussion, what are the key takeaways for beginners?"
)
```

### Local Video Analysis
```python
# Upload and analyze private content
analyze_local_video(
    video_path="/path/to/your/presentation.mp4",
    prompt="Extract the key business metrics and recommendations from this quarterly review"
)
```

### Advanced: Timestamp-Specific Analysis
```python
# Focus on specific video segments
analyze_video_in_session(
    session_id="your-session-id",
    prompt="Analyze the demonstration shown in this segment",
    timestamp_range="5:30-8:45"
)
```

## 🔐 Configuration

### Environment Variables

**Required**:
- `GOOGLE_API_KEY` - Get your free API key from [Google AI Studio](https://ai.google.dev/)

**Optional Configuration**:
```bash
MCP_MAX_SESSIONS=50               # Maximum concurrent sessions
MCP_SESSION_TIMEOUT=7200          # Session timeout in seconds (2 hours)
GEMINI_MODEL_DEFAULT="gemini-2.5-pro-preview-05-06"  # Default Gemini model
MAX_VIDEO_DURATION=7200           # Max video length in seconds
AUTO_CLEANUP_FILES=true           # Auto-cleanup uploaded files
LOG_LEVEL=INFO                    # Logging level (DEBUG, INFO, WARNING, ERROR)
```

### System Requirements
- **Python**: 3.10+ (3.11+ recommended)
- **Memory**: 4GB RAM minimum for video processing
- **Storage**: 500MB for session data and temporary files
- **Network**: High-speed internet for video processing

### Limitations & Constraints

#### YouTube Videos (Unlimited Sessions)
- ✅ **No file size limits** - Direct URL processing
- ✅ **Unlimited session duration** - No 48-hour restriction
- ⚠️ **2-hour video limit** - Recommended maximum for optimal performance
- ⚠️ **Public videos only** - Private/unlisted may not be accessible

#### Local Videos (48-Hour Sessions)
- ⚠️ **2GB file size limit** - Google Files API restriction
- ⚠️ **48-hour auto-deletion** - Files automatically deleted after 48 hours
- ⚠️ **20GB project limit** - Total storage quota per Google project
- ❌ **No retention extension** - Cannot extend file lifespan beyond 48 hours

#### Performance Guidelines
- **Concurrent sessions**: Default maximum 50 active sessions
- **Session timeout**: 2 hours of inactivity before cleanup
- **Memory usage**: ~100MB per active session
- **Response time**: 10-30 seconds for typical analysis requests

## 🧪 Testing

```bash
# Run tests
poetry run pytest

# With coverage
poetry run pytest --cov=youtube_gemini_mcp

# Type checking
poetry run mypy src/
```

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🔗 Related Projects

- [MCP Specification](https://github.com/modelcontextprotocol/specification)
- [Claude Desktop](https://claude.ai/desktop)
- [Google Gemini API](https://ai.google.dev/)

## 📚 Documentation

- **[LLM.md](LLM.md)** - Comprehensive usage guide for AI systems
- **[DEVELOPMENT.md](DEVELOPMENT.md)** - Development setup and architecture
- **[CONTRIBUTING.md](CONTRIBUTING.md)** - How to contribute to the project
- **[PRD.md](PRD.md)** - Complete product requirements document

## 🤝 Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

### Quick Start for Contributors
1. Fork the repository
2. Set up development environment: `poetry install`
3. Run tests: `poetry run pytest`
4. Make your changes and add tests
5. Submit a pull request

## 📞 Support & Community

- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/aigentive/youtube-gemini-mcp/issues)
- 💡 **Feature Requests**: [GitHub Issues](https://github.com/aigentive/youtube-gemini-mcp/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/aigentive/youtube-gemini-mcp/discussions)
- 📖 **Documentation**: Check our comprehensive docs above

### Getting Help

1. **Check Documentation**: Start with [LLM.md](LLM.md) for usage questions
2. **Search Issues**: Look for existing solutions in GitHub Issues
3. **Create Issue**: Provide clear details and reproduction steps
4. **Community**: Join discussions for general questions and ideas