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

Clone the repository:

git clone https://github.com/fakad/video-sum-mcp.git
cd video-sum-mcp

Create and activate conda environment:

conda create -n vsc python=3.8
conda activate vsc

Install dependencies:

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

{
  "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:

python main.py

Usage

Basic Video Processing

# 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:

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

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

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

License

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

Acknowledgments

Built using the Model Context Protocol
OCR powered by PaddleOCR
Platform integrations using various open-source APIs

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

Extracts content from multiple video platforms (Douyin, Bilibili, Xiaohongshu, Zhihu) and generates intelligent knowledge graphs with OCR text recognition capabilities.

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