FFmpeg Media MCP Server MCP Server

About

## FFmpeg MCP Server: Universal Media Processing The **FFmpeg MCP Server** integrates the industry-standard multimedia framework directly into Google Antigravity, enabling AI assistants to transcode, transform, and analyze audio and video content programmatically. This integration brings powerful command-line media processing to your development workflow without manual command construction. ### Why FFmpeg MCP? - **Universal Codec Support**: Process virtually any audio or video format with comprehensive codec library support - **Powerful Transformations**: Apply filters, effects, overlays, and complex processing pipelines - **Stream Manipulation**: Extract, combine, and modify audio and video streams with precision - **Batch Processing**: Process multiple files with consistent settings and automated workflows - **Analysis Tools**: Probe media files for detailed codec, stream, and metadata information ### Key Features #### 1. Video Transcoding ```python from anthropic import Anthropic client = Anthropic() response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[{ "role": "user", "content": "Convert this video to H.264 MP4, 1080p resolution, with AAC audio at 128kbps" }], tools=[{ "name": "ffmpeg_transcode", "description": "Transcode media files" }] ) ``` #### 2. Media Analysis ```python # Probe media information response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[{ "role": "user", "content": "Analyze this video file and tell me the codec, resolution, bitrate, and duration" }], tools=[{"name": "ffmpeg_probe", "description": "Analyze media files"}] ) ``` #### 3. Filter Pipelines ```python # Apply complex filters response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[{ "role": "user", "content": "Add a watermark to the bottom-right corner and apply color correction to this video" }], tools=[{"name": "ffmpeg_filter", "description": "Apply media filters"}] ) ``` #### 4. Audio Processing ```python # Process audio files response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[{ "role": "user", "content": "Extract the audio from this video, normalize volume, and convert to 320kbps MP3" }], tools=[{"name": "ffmpeg_audio", "description": "Process audio content"}] ) ``` ### Configuration ```json { "mcpServers": { "ffmpeg": { "command": "npx", "args": ["-y", "@anthropic/mcp-ffmpeg"], "env": { "FFMPEG_PATH": "/usr/local/bin/ffmpeg" } } } } ``` ### Use Cases **Video Production**: Automate post-production tasks like format conversion, compression, and batch rendering. **Streaming Preparation**: Create HLS/DASH segments and multiple bitrate renditions for adaptive streaming. **Podcast Processing**: Normalize audio levels, remove silence, and convert to distribution formats. **Thumbnail Generation**: Extract frames from videos for preview thumbnails and social media cards. The FFmpeg MCP Server brings industry-standard media processing directly into your development workflow, enabling sophisticated multimedia operations through natural language.

Installation

{
  "mcpServers": {
    "ffmpeg": {
      "mcpServers": {
        "ffmpeg": {
          "args": [
            "-y",
            "@bitscorp/mcp-ffmpeg"
          ],
          "command": "npx"
        }
      }
    }
  }
}

How to Use

1. 1Video conversion
2. 2Audio processing
3. 3Streaming support