Media_MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Media_MCPextract subtitles from this video"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Modular MCP Media Project
This project has been refactored into two independent local MCP-style CLI servers:
image_mcp/for image processingvideo_mcp/for video processing
Each server is modularized into a main.py plus a services/ package so you can add new capabilities without mixing concerns.
Project structure
mcp_server/
├── image_mcp/
│ ├── __init__.py
│ ├── main.py
│ └── services/
│ ├── __init__.py
│ ├── bg_removal.py
│ ├── compress.py
│ ├── resize.py
│ ├── upscale.py
│ ├── convert.py
│ ├── crop.py
│ ├── watermark.py
│ ├── face_blur.py
│ ├── ocr.py
│ └── common.py
├── video_mcp/
│ ├── __init__.py
│ ├── main.py
│ └── services/
│ ├── __init__.py
│ ├── compress.py
│ ├── trim.py
│ ├── gif.py
│ ├── merge.py
│ ├── audio.py
│ ├── thumbnail.py
│ ├── subtitle.py
│ └── common.py
├── app.py
├── requirements.txt
├── README.md
├── input/
├── output/Related MCP server: media-mcp
Install dependencies
pip install -r requirements.txtInstall FFmpeg on the machine and ensure it is available in PATH.
Image MCP usage
python image_mcp/main.py bg --input input/in.png --output output/out.png
python image_mcp/main.py resize --input input/in.jpg --output output/out.jpg --width 800 --height 600
python image_mcp/main.py upscale --input input/in.jpg --output output/out.jpg
python image_mcp/main.py compress --input input/in.jpg --output output/out.jpg --quality 60
python image_mcp/main.py convert --input input/in.png --output output/out.jpg
python image_mcp/main.py crop --input input/in.jpg --output output/out.jpg --x1 10 --y1 10 --x2 200 --y2 200
python image_mcp/main.py watermark --input input/in.jpg --output output/out.jpg --text "Demo"
python image_mcp/main.py faceblur --input input/in.jpg --output output/out.jpg
python image_mcp/main.py ocr --input input/in.jpgVideo MCP usage
python video_mcp/main.py compress --input input/in.mp4 --output output/out.mp4
python video_mcp/main.py trim --input input/in.mp4 --output output/out.mp4 --start 00:00:05 --end 00:00:10
python video_mcp/main.py gif --input input/in.mp4 --output output/out.gif
python video_mcp/main.py merge --inputs input/v1.mp4 input/v2.mp4 --output output/out.mp4
python video_mcp/main.py audio --input input/in.mp4 --output output/out.mp3
python video_mcp/main.py thumbnail --input input/in.mp4 --output output/thumb.jpg
python video_mcp/main.py subtitle --input input/in.mp4 --output output/out.srtDesign notes
Each command is implemented in a dedicated service module.
Shared validation and file-size logging is centralized in the service common files.
Both servers expose a simple argparse CLI and can be adapted into HTTP APIs later.
The architecture is intentionally local-first and easy to extend.
This server cannot be deployed
Maintenance
Related MCP Connectors
MCP server for Clipkit — gives AI agents a video toolbox via the Clipkit schema.
Hosted MCP tools for FFmpeg-style video and audio processing through FFMPEG API.
MCP server for Qwen Image 3 AI image generation
MCP server for Google Veo AI video generation
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