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qualityA Model Context Protocol server that enables AI assistants to extract transcripts from YouTube videos, allowing AI to analyze and work with video content directly.
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Offers integration with GitHub for project management, contributions, and documentation access for the video editing MCP server.
Provides specific configuration instructions for Claude Desktop integration on macOS systems.
Utilizes Python 3.10+ as the foundation for all video and audio processing operations, with compatibility requirements specified.
Enables downloading videos from YouTube for further editing and processing within the MCP server's workflow.
Video Edit MCP Server 🎬
A powerful Model Context Protocol (MCP) server designed for advanced video and audio editing operations. This server enables MCP clients—such as Claude Desktop, Cursor, and others—to perform comprehensive multimedia editing tasks through a standardized and unified interface.
https://github.com/user-attachments/assets/134b8b82-80b1-4678-8930-ab53121b121f
✨ Key Features
🎥 Video Operations
Basic Editing: Trim, merge, resize, crop, rotate videos
Effects: Speed control, fade in/out, grayscale, mirror
Overlays: Add text, images, or video overlays with transparency
Format Conversion: Convert between formats with codec control
Frame Operations: Extract frames, create videos from images
🎵 Audio Operations
Audio Processing: Extract, trim, loop, concatenate audio
Volume Control: Adjust levels, fade in/out effects
Audio Mixing: Mix multiple tracks together
Integration: Add audio to videos, replace soundtracks
📥 Download & Utilities
Video Download: Download from YouTube and other platforms
File Management: Directory operations, file listing
Path Suggestions: Get recommended download locations
🧹 Memory & Cleanup
Smart Memory: Chain operations without saving intermediate files
Resource Management: Clear memory, check stored objects
Efficient Processing: Keep objects in memory for complex workflows
🔗 Operation Chaining
Seamlessly chain multiple operations together without creating intermediate files. Process your video through multiple steps (trim → add audio → apply effects → add text) while keeping everything in memory for optimal performance.
📋 Requirements
Python 3.10 or higher
moviepy==1.0.3
yt-dlp>=2023.1.6
mcp>=1.12.2
typing-extensions>=4.0.0
⚙️ Installation & Setup
For Claude Desktop / Cursor MCP Integration
Ensure that
If not, install it using the following PowerShell command:
powershell -ExecutionPolicy Bypass -Command "irm https://astral.sh/uv/install.ps1 | iex"
Add this configuration to your MCP configuration file:
{
"mcpServers": {
"video_editing": {
"command": "uvx",
"args": [
"--python",
"3.11",
"video-edit-mcp"
]
}
}
}
Configuration file locations:
Claude Desktop (Windows):
%APPDATA%/Claude/claude_desktop_config.jsonClaude Desktop (macOS):
~/Library/Application Support/Claude/claude_desktop_config.jsonCursor:
.cursor/mcp.jsonin your project root
Manual Installation
git clone https://github.com/Aditya2755/video-edit-mcp.git
cd video-edit-mcp
pip install -r requirements.txt
pip install -e .
🏗️ Project Structure
video_edit_mcp/
├── src/
│ └── video_edit_mcp/
│ ├── __init__.py
│ ├── main.py # MCP server implementation
│ ├── video_operations.py # Video editing tools
│ ├── audio_operations.py # Audio processing tools
│ ├── download_utils.py # Download functionality
│ ├── util_tools.py # Memory & utility tools
│ ├── utils.py # Utility functions
│
├── pyproject.toml # Project configuration
├── requirements.txt # Dependencies
├── uv.lock # Lock file
├── LICENSE # MIT License
├── MANIFEST.in # Manifest file
└── README.md
🎯 Example Usage
# Chain operations without intermediate files
video_info = get_video_info("input.mp4")
trimmed = trim_video("input.mp4", 10, 60, return_path=False) # Keep in memory
with_audio = add_audio(trimmed, "background.mp3", return_path=False)
final = add_text_overlay(with_audio, "Hello World", x=100, y=50, return_path=True)
🚀 Future Enhancements & Contributions
We welcome contributions in these exciting areas:
🤖 AI-Powered Features
Speech-to-Text (STT): Automatic subtitle generation and transcription
Text-to-Speech (TTS): AI voice synthesis for narration
Audio Enhancement: AI-based noise reduction and audio quality improvement
Smart Timestamps: Automatic scene detection and chapter generation
Face Tracking: Advanced face detection and tracking for automatic editing
Object Recognition: Track and edit based on detected objects
Content Analysis: AI-powered content categorization and tagging
🤝 Contributing
Fork the repository
Create a feature branch:
git checkout -b feature/amazing-featureCommit your changes:
git commit -m 'Add amazing feature'Push to the branch:
git push origin feature/amazing-featureOpen a Pull Request
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Made with ❤️ for the AI and multimedia editing community
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A Model Context Protocol server that enables AI assistants to perform comprehensive video and audio editing operations including trimming, effects, overlays, audio processing, and YouTube downloads.
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