MCP Video Extraction

🚀 MCP Video & Audio Text Extraction Server

An MCP server that offers text extraction capabilities from diverse video platforms and audio files. It implements the Model Context Protocol (MCP) to provide standardized access to audio transcription services.

🚀 Quick Start

This MCP server is designed to extract text from various video platforms and audio files. It adheres to the Model Context Protocol, enabling standardized access to audio transcription services.

✨ Features

• High - quality speech recognition based on Whisper
• Multi - language text recognition
• Support for various audio formats (mp3, wav, m4a, etc.)
• MCP - compliant tools interface
• Asynchronous processing for large files

📦 Installation

Using uv (recommended)

When using uv, no specific installation is required. We'll use uvx to directly run the video extraction server:

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

Install FFmpeg

FFmpeg is essential for audio processing. You can install it via different methods:

# Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# Arch Linux
sudo pacman -S ffmpeg

# MacOS
brew install ffmpeg

# Windows (using Chocolatey)
choco install ffmpeg

# Windows (using Scoop)
scoop install ffmpeg

💻 Usage Examples

Configure for Claude/Cursor

Add the following to your Claude/Cursor settings:

"mcpServers": {
  "video-extraction": {
    "command": "uvx",
    "args": ["mcp-video-extraction"]
  }
}

Available MCP Tools

1. Video download: Download videos from supported platforms
2. Audio download: Extract audio from videos on supported platforms
3. Video text extraction: Extract text from videos (download and transcribe)
4. Audio file text extraction: Extract text from audio files

📚 Documentation

Supported Platforms

This service supports downloading videos and extracting audio from various platforms, including but not limited to:

• YouTube
• Bilibili
• TikTok
• Instagram
• Twitter/X
• Facebook
• Vimeo
• Dailymotion
• SoundCloud

For a complete list of supported platforms, please visit [yt - dlp supported sites](https://github.com/yt - dlp/yt - dlp/blob/master/supportedsites.md).

Core Technology

This project uses OpenAI's Whisper model for audio - to - text processing through MCP tools. The server exposes four main tools:

1. Video download: Download videos from supported platforms
2. Audio download: Extract audio from videos on supported platforms
3. Video text extraction: Extract text from videos (download and transcribe)
4. Audio file text extraction: Extract text from audio files

MCP Integration

This server is built using the Model Context Protocol, which provides:

• A standardized way to expose tools to LLMs
• Secure access to video content and audio files
• Integration with MCP clients like Claude Desktop

Tech Stack

• Python 3.10+
• Model Context Protocol (MCP) Python SDK
• yt - dlp (YouTube video download)
• openai - whisper (Core audio - to - text engine)
• pydantic

System Requirements

• FFmpeg (Required for audio processing)
• Minimum 8GB RAM
• Recommended GPU acceleration (NVIDIA GPU + CUDA)
• Sufficient disk space (for model download and temporary files)

Configuration

The service can be configured through environment variables:

Whisper Configuration

• WHISPER_MODEL: Whisper model size (tiny/base/small/medium/large), default: 'base'
• WHISPER_LANGUAGE: Language setting for transcription, default: 'auto'

YouTube Download Configuration

• YOUTUBE_FORMAT: Video format for download, default: 'bestaudio'
• AUDIO_FORMAT: Audio format for extraction, default: 'mp3'
• AUDIO_QUALITY: Audio quality setting, default: '192'

Storage Configuration

• TEMP_DIR: Temporary file storage location, default: '/tmp/mcp - video'

Download Settings

• DOWNLOAD_RETRIES: Number of download retries, default: 10
• FRAGMENT_RETRIES: Number of fragment download retries, default: 10
• SOCKET_TIMEOUT: Socket timeout in seconds, default: 30

Performance Optimization Tips

1. GPU Acceleration:

   • Install CUDA and cuDNN
   • Ensure the GPU version of PyTorch is installed

2. Model Size Adjustment:

   • tiny: Fastest but lower accuracy
   • base: Balanced speed and accuracy
   • large: Highest accuracy but requires more resources

3. Use SSD storage for temporary files to improve I/O performance

Notes

• The Whisper model (approximately 1GB) needs to be downloaded on the first run.
• Ensure sufficient disk space for temporary audio files.
• A stable network connection is required for YouTube video downloads.
• A GPU is recommended for faster audio processing.
• Processing long videos may take considerable time.

MCP Integration Guide

This server can be used with any MCP - compatible client, such as:

• Claude Desktop
• Custom MCP clients
• Other MCP - enabled applications

For more information about MCP, visit Model Context Protocol.

Documentation

For the Chinese version of this documentation, please refer to README_zh.md

🔧 Technical Details

The project uses OpenAI's Whisper model through MCP tools for audio - to - text processing. The server is built on the Model Context Protocol, which standardizes tool access for LLMs, provides secure access to media files, and enables integration with MCP clients.

📄 License

MIT