This video editing MCP server, developed by Kush Agrawal, enables AI assistants to perform a wide range of video editing operations using FFmpeg. Built with Python and leveraging the MCP framework, it provides a single powerful tool, 'execute_ffmpeg', which validates and executes FFmpeg commands with real-time progress tracking. The server supports operations like trimming, merging, format conversion, speed adjustment, audio manipulation, and subtitle addition. By abstracting complex FFmpeg operations into a natural language interface, it allows AI systems to easily manipulate video content. This implementation is particularly useful for automating video editing tasks, content creation, and media processing workflows, making it suitable for applications in video production, social media content generation, and automated video analysis.
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Highlight AIExecutes FFmpeg commands with progress tracking. Takes a command string as input, validates and executes FFmpeg operations, reports real-time progress, handles errors, and supports various FFmpeg operations including trimming, merging, converting formats, adjusting speed, adding audio tracks, extracting audio, adding subtitles, and applying basic filters.
