video-sum-mcp

# Video Content Summarization MCP Server A Model Context Protocol (MCP) server that extracts content from multiple video platforms and generates intelligent knowledge graphs. ## Features ### 🌐 Multi-Platform Support - **Douyin (TikTok China)** - Short video content extraction - **Bilibili** - Video and live streaming content - **Xiaohongshu (Little Red Book)** - Social media posts with OCR support - **Zhihu** - Q&A platform content ### ✨ Advanced Capabilities - **OCR Text Recognition** - Extract text from images using PaddleOCR - **Knowledge Graph Generation** - Intelligent content structuring - **Chinese Content Optimization** - Specialized processing for Chinese text - **Context-Aware Extraction** - Smart content understanding and quality control ## Installation ### Prerequisites - Python 3.8 or higher - Anaconda (recommended for dependency management) ### Setup 1. Clone the repository: ```bash git clone https://github.com/fakad/video-sum-mcp.git cd video-sum-mcp ``` 2. Create and activate conda environment: ```bash conda create -n vsc python=3.8 conda activate vsc ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` ## Configuration ### For Claude Desktop Add this configuration to your Claude Desktop config file: **macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json` **Windows:** `%APPDATA%/Claude/claude_desktop_config.json` ```json { "mcpServers": { "video-sum-mcp": { "command": "python", "args": ["/path/to/video-sum-mcp/main.py"], "cwd": "/path/to/video-sum-mcp", "env": { "CONDA_DEFAULT_ENV": "vsc" } } } } ``` ### For Other MCP Clients The server can be started directly: ```bash python main.py ``` ## Usage ### Basic Video Processing ```python # Example: Process a Bilibili video result = process_video( url="https://www.bilibili.com/video/BV1234567890", output_format="markdown" ) ``` ### Supported URL Formats - **Douyin:** `https://v.douyin.com/...` or full URLs - **Bilibili:** `https://www.bilibili.com/video/...` - **Xiaohongshu:** `https://www.xiaohongshu.com/discovery/item/...` - **Zhihu:** `https://www.zhihu.com/question/...` ### Context-Enhanced Processing For platforms with anti-crawling measures, you can provide context: ```python result = process_video( url="https://...", context_text="Additional context information..." ) ``` ## Features in Detail ### OCR Integration - Automatic image text extraction from Xiaohongshu posts - PaddleOCR for accurate Chinese character recognition - Batch processing for multiple images ### Knowledge Graph Generation - Structured content analysis - Intelligent relationship mapping - Quality control and validation ### Anti-Crawling Strategies - Smart fallback mechanisms - Context-based extraction - User guidance for optimal results ## Development ### Project Structure ``` video-sum-mcp/ ├── core/ # Core functionality modules │ ├── extractors/ # Platform-specific extractors │ ├── processors/ # Content processing logic │ ├── knowledge_graph/ # Knowledge graph generation │ └── managers/ # Resource management ├── scripts/ # MCP server implementation ├── main.py # Main entry point ├── requirements.txt # Python dependencies └── pyproject.toml # Project configuration ``` ### Running Tests ```bash python -m pytest ``` ## Dependencies Key dependencies include: - `bilibili-api-python` - Bilibili API integration - `yt-dlp` - Video downloading capabilities - `PaddleOCR` - OCR text recognition - `beautifulsoup4` - Web scraping - `requests` - HTTP requests See `requirements.txt` for complete list. ## Contributing 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 ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgments - Built using the [Model Context Protocol](https://modelcontextprotocol.io/) - OCR powered by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) - Platform integrations using various open-source APIs