ffmpeg-mcp
Enables comprehensive video and audio processing using FFmpeg, supporting tasks like metadata extraction, clipping, scaling, and adding transitions or overlays. It provides a high-performance interface for building media processing microservices via FastMCP.
README

ffmpeg-mcp 🎬⚡
A Python package for media processing using FFmpeg and FastMCP. It enables building microservices that handle video/audio tasks with clean, reusable interfaces.
📖 Overview
This project provides a framework for handling media processing tasks using:
- FFmpeg — A powerful multimedia framework for processing audio and video files
- FastMCP — A high-performance framework for building microservices
🛠️ Available Tools
1. Metadata & Frames
-
get_video_metadata-
param(s):
input_video_path: str
-
-
extract_frames-
params:
input_video_path: str | Pathnumber_of_frames: intframe_timestamps: int (eg: 5s, 10s, 15s, ...)
-
2. Audio
-
extract_audio-
param(s):
input_video_path: str
-
3. Video Scaling & Resizing
-
scale_video-
params:
input_video_path: strresolution: Optional[str]
-
4. Overlay Operations
-
overlay_image-
params:
input_video_path: stroverlay_image_path: strpositioning: Literal[top_left, bottom_left, top_right, bottom_right, center, top_center, bottom_center] = 'top_right'scale: tuple[int, int] | None = (100, 100)keep_audio: bool = Trueopacity: float | None = None (range 0.0–1.0)start_time: float = 0.0 (in seconds)duration: float | None = None (in seconds; None = until end of video)
-
-
overlays_video-
params:
input_video_path: stroverlay_video_path: strpositioning: Literal[top_left, bottom_left, top_right, bottom_right] = 'top_left'
-
5. Video Editing
-
clip_video-
params:
input_video_path: strstart_timestampduration: int
-
-
crop_video-
params:
input_video_pathsafe_crop: boolheight: intwidth: intx_offset: inty_offset: int
-
-
trim_and_concatenate-
params:
input_video_pathnumber_of_trims: inttrim_timestamp: List[(start, end), (start, end), ...]
-
-
make_gif-
params:
input_video_pathstart_timestampduration
-
6. Concatenation & Transitions
-
concatenate_videos-
param(s):
file_list: list[Path]
-
-
normalize_video_clips-
params:
input_video_clips: List[str]resolution: tuple default(1280, 720)frame_rate: int default30crf: int default23audio_bitrate: str default128kpreset: str defaultfast
-
-
concat_clips_with_transition-
params:
input_video_clips: List[str]transition_types: str defaultfade(e.g., fade, wipeleft, rectcrop, coverup, etc.)transition_duration: float default2
-
🧰 Utilities
The utils folder contains helper functions and decorators to enhance the functionality and robustness of the media processing tools.
a. Decorators
validate_input_video_pathA decorator that checks if the video path exists, is non-empty, and is a valid video file. This ensures that all video processing functions receive a valid input file.
📦 Requirements
- Python 3.12 or higher
- uv (package manager)
- FFmpeg installed on the system
🚀 Usage
The package can be used to build media processing microservices that leverage the power of FFmpeg through a Python interface.
1. Clone this repo
git clone git@github.com:yubraaj11/ffmpeg-mcp.git
2. Sync the project
uv sync --frozen
3. Use via MCP - Cline config
{
"mcpServers": {
"ffmpeg-mcp": {
"autoApprove": [],
"disabled": false,
"timeout": 60,
"command": "uv",
"args": [
"--directory",
"/path/to/ffmpeg-mcp/ffmpeg_mcp",
"run",
"main.py"
],
"env": {
"PYTHONPATH": "/path/to/ffmpeg-mcp"
},
"transportType": "stdio"
}
}
}
📚 Dependencies
ffmpeg-python— Python bindings for FFmpegfastmcp— Framework for building microservicescolorlog— Colored logging outputfastapi— Web framework for building APIspydantic— Data validation and settings management
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.