ChatCut MCP Server 🎬🤖
ChatCut MCP Server is a Model Context Protocol (MCP) server that empowers AI coding agents (such as Claude Code, Cursor, Codex, or Antigravity) with local video editing, media processing, transcription, and hardware-accelerated rendering capabilities.
With this server, you can give your AI agent natural language instructions like "trim the silences from this video, overlay slide 2 at second 20, and render it using my GPU" or "transcribe this video, find the most engaging segment, and create a vertical 9:16 Short", and the agent will know exactly which tools to call to physically process the video on your local machine.
🛠️ Tools Reference
The server automatically registers the following tools with the MCP client:
| Tool Name | Arguments | Description |
|---|---|---|
detect_hardware |
None | Auto-detects the system's active GPU (NVIDIA, Apple Silicon, Intel, AMD) and checks if FFmpeg has hardware encoders enabled. |
parse_video_guidelines_excel |
file_path: str |
Parses an Excel video editing guidelines file and outputs the cut ranges and slide overlay times in seconds. |
convert_pptx |
pptx_path: str, output_dir: str |
Converts a PowerPoint presentation to PNG slides and pre-scales them to $1920 \times 1080$ in a cache directory (significantly speeds up FFmpeg rendering). |
transcribe_video_file |
video_path: str, gemini_api_key: str (optional), force_local: bool (optional) |
Transcribes a video file to JSON with timestamps. Uses the Gemini API (cloud-based, free/fast) or local Whisper (faster-whisper running on CPU or GPU). |
find_short_clips |
transcript_json: str, gemini_api_key: str (optional) |
Analyzes the transcript text and identifies the 3 best 30-60 second clips for vertical Shorts/Reels/TikToks with hook explanations. |
generate_vertical_crop_timeline |
timeline_json_path: str, start_time: float, end_time: float, crop_type: str (optional) |
Creates a vertical (9:16) 1080x1920 timeline configuration, cropping or fitting the main video for the specified time segment. |
render_video_project |
timeline_json_path: str, output_mp4_path: str |
Renders the final video from the timeline configuration using FFmpeg with GPU-accelerated encoding (e.g. -c:v h264_nvenc). |
🤖 INSTRUCTIONS FOR AI AGENTS (Claude Code / Codex / Antigravity)
If you are an AI agent reading this repository and the user has asked you to "Install this MCP server", follow these steps based on the user's operating system:
Step 1: Check System Dependencies
Ensure the user's system has python (>= 3.10), ffmpeg, and optionally libreoffice (required for .pptx conversions) installed. If they are missing, inform the user or request permission to install them.
Step 2: Locate the MCP Client Configuration File
Locate the configuration file corresponding to the application the user is using to converse with you:
- Claude Desktop (Windows):
%APPDATA%\Claude\claude_desktop_config.json - Claude Desktop (macOS):
~/Library/Application Support/Claude/claude_desktop_config.json - Claude Desktop (Linux):
~/.config/Claude/claude_desktop_config.json - Claude Code (Global CLI):
~/.claude.json - Cursor (IDE): Configurable in the Settings -> Features -> MCP section of the graphical interface.
Step 3: Add the Server to Configuration
Read the configuration JSON file and append a new entry under the "mcpServers" key.
Option A: Install from PyPI (Recommended for production)
"chatcut": {
"command": "uvx",
"args": ["chatcut-mcp-server"],
"env": {
"GEMINI_API_KEY": "YOUR_OPTIONAL_API_KEY"
}
}
Option B: Install from Local Source (Recommended for development/WSL)
"chatcut-local": {
"command": "wsl",
"args": [
"-d", "Ubuntu",
"-u", "parrot",
"bash", "-c",
"cd /home/parrot/video && venv/bin/chatcut-mcp-server"
],
"env": {
"GEMINI_API_KEY": "YOUR_OPTIONAL_API_KEY"
}
}
Write the configuration back, preserving all other existing keys.
👨💻 HUMAN INSTALLATION GUIDE
Option A: Automatic Running (Recommended)
If you have uv installed on your system, you can run the server instantly without a permanent installation using uvx:
uvx chatcut-mcp-server
Option B: Local Source Installation
- Clone the repository:
git clone git@github.com:madrigal-a/chatcut-mcp-server.git cd chatcut-mcp-server
- Create a virtual environment and install the package in editable mode:
python -m venv venv source venv/bin/activate # On Windows use: venv\Scripts\activate pip install -e .
- Run the server:
chatcut-mcp-server
📝 Additional System Requirements
- LibreOffice headless: Required for automated PPTX to PDF conversions. On Ubuntu, install it using
sudo apt-get install libreoffice. - Poppler-utils: Required by
pdf2imageto extract pages from PDF files. On Ubuntu, install it usingsudo apt-get install poppler-utils.
Metadata
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Total release size: 96.6 kB
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