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FFmpeg-MCP

Community
video-creator

Using ffmpeg command line to achieve an mcp server, can be very convenient, through the dialogue to achieve the local video search, tailoring, stitching, playback,clip, overlay, concat and other functions

Publishervideo-creator
Repositoryffmpeg-mcp
LanguagePython
Forks
23
Stars
134
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    FFmpeg-MCP exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    134 stars and 23 forks from the linked repository.

FFmpeg-MCP

Using ffmpeg command line to achieve an mcp server, can be very convenient, through the dialogue to achieve the local video search, tailoring, stitching, playback and other functions

Support Tools

The server implements the following tools:

  • find_video_path The parameters are directory and file name, file name can be complete, or is not suffixed, recursive search in the directory, return the full path
  • get_video_info The parameters are video path, return the video info, linkes duration/fps/codec/width/height.
  • clip_video The parameter is the file path, start time, end time or duration, and returns the trimmed file path
  • concat_videos The parameters are the list of files, the output path, and if the video elements in the list of files, such as width, height, frame rate, etc., are consistent, quick mode synthesis is automatically used
  • play_video Play video/audio with ffplay, support many format, like mov/mp4/avi/mkv/3gp, video_path: video path speed: play rate loop: play count
  • overlay_video Two video overlay. background_video: backgroud video path overlay_video: front video path output_path: output video path position: relative location dx: x offset dy: y offset
  • scale_video Video scale. video_path: in video path width: out video width, -2 keep aspect height: out video height, -2 keep aspect output_path: output video path
  • extract_frames_from_video Extract images from a video. Parameters: video_path (str): The path to the video. fps (int): Extract one frame every specified number of seconds. If set to 0, extract all frames; if set to 1, extract one frame per second. output_folder (str): The directory where the images will be saved. format (int): The format of the extracted images; 0: PNG, 1: JPG, 2: WEBP. total_frames (int): The maximum number of frames to extract. If set to 0, there is no limit

More features are coming

Installation procedure

  1. Download project
git clone  https://github.com/video-creator/ffmpeg-mcp.git
cd ffmpeg-mcp
uv sync
  1. Configuration in Cline
{
  "mcpServers": {
    "ffmpeg-mcp": {
      "autoApprove": [],
      "disabled": false,
      "timeout": 60,
      "command": "uv",
      "args": [
        "--directory",
        "/Users/xxx/Downloads/ffmpeg-mcp",
        "run",
        "ffmpeg-mcp"
      ],
      "transportType": "stdio"
    }
  }
}

Note: the value:/Users/XXX/Downloads/ffmpeg in args need to replace the actual download ffmpeg-mcp directory

Supported platforms

Currently, only macos platforms are supported, including ARM64 or x86_64

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "video-creator-ffmpeg-mcp": {
      "command": "",
      "args": []
    }
  }
}

Use FFmpeg-MCP MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once FFmpeg-MCP is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add FFmpeg-MCP as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the FFmpeg-MCP MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "video-creator-ffmpeg-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "ffmpeg-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the FFmpeg-MCP MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the FFmpeg-MCP MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use FFmpeg-MCP to help me with this task?
FFmpeg-MCP
Sure. I read it.
Here is what I found using FFmpeg-MCP.

Frequently asked questions

What is the FFmpeg-MCP MCP server used for?

FFmpeg-MCP is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use FFmpeg-MCP MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use FFmpeg-MCP with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use FFmpeg-MCP MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With FFmpeg-MCP connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect FFmpeg-MCP MCP to TypingMind?

FFmpeg-MCP runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does FFmpeg-MCP MCP provide in TypingMind?

FFmpeg-MCP exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use FFmpeg-MCP MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If FFmpeg-MCP requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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