Model Context Protocol (MCP) server for local video editing, presentation overlays, transcription, and hardware-accelerated rendering.
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# ChatCut MCP Server 🎬🤖 [](https://opensource.org/licenses/MIT) [](https://www.python.org/) [](https://modelcontextprotocol.io/) **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`…
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