MCP Video & Audio Text Extraction Server: Model Context Protocol Integration

Overview: What is the MCP Video & Audio Text Extraction Server?

The MCP Video & Audio Text Extraction Server is an advanced tool that leverages the power of the Model Context Protocol (MCP) to provide high-quality text extraction capabilities from a wide range of video and audio sources. This server uses OpenAI's Whisper model for accurate speech-to-text processing, making it a versatile solution for various content analysis needs across multiple platforms.

🔧 Core Features & MCP Capabilities

The core features of this server are designed to seamlessly integrate with AI applications, offering standardized access to a suite of tools that include video download, audio extraction, and text transcription. These capabilities are powered by the Model Context Protocol (MCP) for secure and efficient data processing.

Support for Various Platforms

This service supports downloading videos and extracting audio from numerous platforms, including:

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

A complete list of supported platforms is available here.

High-Quality Speech Recognition with Whisper Model

The text extraction tool is built on top of the OpenAI Whisper model, which is renowned for its high-quality speech recognition capabilities. Whether handling multilingual content or dealing with various audio formats like mp3, wav, and m4a, this server ensures accurate transcription.

MCP-Compliant Interface

The server exposes a standardized interface through MCP tools, making it easy to integrate into existing AI workflows. This includes the ability to securely access video content and audio files, ensuring compatibility with MCP clients like Claude Desktop, Continue, Cursor, and others.

⚙️ MCP Architecture & Protocol Implementation

This project is built using Python 3.9+ and leverages several key technologies:

• Python: The primary programming language for server development.
• MCP Python SDK: For exposing tools to LLMs (Large Language Models) in a standardized way.
• yt-dlp: A powerful command-line download utility that supports a multitude of platforms.
• openai-whisper: Core audio-to-text processing engine.
• pydantic: Data validation and settings management.

The MCP architecture ensures secure access to video content and audio files, enabling seamless integration with MCP clients. The server can be started using the following command:

python server.py

🚀 Getting Started with Installation

To set up this server on your environment, follow these steps:

1. Clone the repository:

   git clone [repository-url]
   cd mcp-ytb-text-extraction
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install FFmpeg (if not already installed):

   # 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
   

💡 Key Use Cases in AI Workflows

This server is particularly valuable in scenarios where accurate text extraction from video and audio content is necessary. Here are two practical use cases:

Use Case 1: Content Moderation

Large-scale platforms often need to moderate user-generated content for compliance with community guidelines. By integrating this server into their workflow, they can automate the transcription process, facilitating easier moderation.

Technical Implementation:

• Video and audio files are downloaded using the video_download tool.
• Transcriptions are generated by the audio_extract or video_extract tools.
• Automated moderation systems analyze the text to ensure content adherence.

Use Case 2: Educational Content Analysis

In educational settings, analyzing lecture videos can provide valuable insights into student engagement and learning outcomes. This server can assist in this process by providing accurate transcripts of lectures.

Technical Implementation:

• Videos are downloaded using the video_download tool.
• Transcriptions are created with the video_extract or audio_extract tools to enable further analysis.

🔌 Integration with MCP Clients

Compatibility with various MCP clients is crucial for broad use and deployment. The following table outlines the current support status:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

For more information about MCP, visit Model Context Protocol.

📊 Performance & Compatibility Matrix

The server performs well across a wide range of platforms and file types. Here are some performance metrics:

• FFmpeg: Essential for processing various audio formats.
• System Requirements:
  • RAM: Minimum 8GB, recommended higher for larger files.
  • GPU: NVIDIA GPU + CUDA for faster processing when enabled.

🛠️ Advanced Configuration & Security

Configuration can be modified in the config.yaml file. Key parameters include:

server:
  host: localhost
  port: 8080
service:
  whisper:
    model: base
    language: auto
  audio:
    format: mp3
    quality: 192k
temp_dir: /tmp/mcp-video

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.

❓ Frequently Asked Questions (FAQ)

How do I install the required dependencies?

You can install all necessary packages by running:

pip install -r requirements.txt

Can I change the output directory for audio and video downloads?

Yes, you can modify the output_dir parameter in the configuration file to specify a different download path.

What formats does this server support for audio files?

This server supports mp3, wav, and m4a formats. Other formats might require additional processing.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! To get started contributing, make sure you follow these guidelines:

1. Clone the repository.
2. Set up the development environment as described earlier.
3. Make your contributions and submit a pull request.

For more details on how to contribute, refer to the CONTRIBUTING.md file in the repository.

🌐 MCP Ecosystem & Resources

Visit the official Model Context Protocol documentation for more information:

• MCP Introduction

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

MCP Protocol Flow Diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Client Compatibility Matrix:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

MCP Configuration Sample:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This comprehensive documentation positions the MCP Video & Audio Text Extraction Server as a robust tool for AI applications, emphasizing its versatility and ease of integration with existing workflows.