ffmpeg_mcp

radzevich/ffmpeg_mcp

3.2

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The FFmpeg MCP Server is a secure and isolated environment for performing video and audio processing tasks using FFmpeg, integrated with Google Cloud Storage.

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FFmpeg MCP Server

A Model Context Protocol (MCP) server that provides secure FFmpeg functionality through a sandboxed environment. This server allows AI assistants and other MCP clients to perform video/audio processing tasks using FFmpeg in an isolated environment.

Features

  • Sandboxed Execution: All FFmpeg commands run in isolated temporary directories for security
  • File Management: Upload, download, and manage files within the sandbox
  • Google Cloud Storage Integration: Direct integration with GCS for file transfers
  • Security: Only FFmpeg commands are allowed, preventing arbitrary code execution
  • RESTful API: Runs as an HTTP server using FastMCP

Installation

Prerequisites

  • Python 3.11 or higher
  • FFmpeg installed on your system
  • (Optional) Google Cloud credentials for GCS features

Setup

  1. Clone the repository:
git clone <repository-url>
cd ffmpeg_mcp
  1. Install dependencies using uv (recommended):
uv sync

Or using pip:

pip install -e .
  1. (Optional) Set up Google Cloud credentials for GCS integration:
export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/credentials.json"

Usage

Starting the Server

python main.py

The server will start on localhost:8000 by default.

Available Tools

1. create_sandbox()

Creates a new isolated sandbox environment for FFmpeg operations.

Returns: Sandbox directory path

2. run_ffmpeg_command(sandbox, command)

Executes FFmpeg commands within the specified sandbox.

Parameters:

  • sandbox (str): Sandbox directory path
  • command (str): FFmpeg command to execute

Returns: Command output or error message

3. put_file(sandbox, filename, content)

Puts a file into the sandbox environment.

Parameters:

  • sandbox (str): Sandbox directory path
  • filename (str): Name of the file to create
  • content (bytes): File content

Returns: Full path of the created file

4. get_file(sandbox, filename)

Retrieves a file from the sandbox environment.

Parameters:

  • sandbox (str): Sandbox directory path
  • filename (str): Name of the file to retrieve

Returns: File content as bytes

5. delete_file(sandbox, filename)

Deletes a file from the sandbox environment.

Parameters:

  • sandbox (str): Sandbox directory path
  • filename (str): Name of the file to delete

Returns: Confirmation message

6. download_file(sandbox, url, filename)

Downloads a file from a URL into the sandbox.

Parameters:

  • sandbox (str): Sandbox directory path
  • url (str): URL of the file to download
  • filename (str): Local filename to save as

Returns: Full path of the downloaded file

7. upload_file(sandbox, filename, upload_url)

Uploads a file from the sandbox to a specified URL.

Parameters:

  • sandbox (str): Sandbox directory path
  • filename (str): Name of the file to upload
  • upload_url (str): Destination URL

Returns: Upload response

8. download_file_from_gcs(sandbox, gcs_url, filename)

Downloads a file from Google Cloud Storage.

Parameters:

  • sandbox (str): Sandbox directory path
  • gcs_url (str): GCS URL (gs://bucket/path)
  • filename (str): Local filename to save as

Returns: Full path of the downloaded file

9. upload_file_to_gcs(sandbox, filename, gcs_url)

Uploads a file to Google Cloud Storage.

Parameters:

  • sandbox (str): Sandbox directory path
  • filename (str): Name of the file to upload
  • gcs_url (str): Destination GCS URL

Returns: Confirmation message

Example Workflow

# 1. Create a sandbox
sandbox = create_sandbox()

# 2. Download a video file
download_file(sandbox, "https://example.com/video.mp4", "input.mp4")

# 3. Process with FFmpeg
run_ffmpeg_command(sandbox, "ffmpeg -i input.mp4 -vf scale=720:480 output.mp4")

# 4. Retrieve the processed file
processed_video = get_file(sandbox, "output.mp4")

Security Features

  • Command Restriction: Only commands starting with "ffmpeg" are allowed
  • Sandbox Isolation: All operations are contained within temporary directories
  • Path Validation: Sandbox directories are validated before operations
  • Error Handling: Comprehensive error handling for failed operations

Configuration

The server runs on localhost:8000 by default. You can modify the host and port in the main.py file:

if __name__ == "__main__":
    mcp.run(transport="httpx", host="your-host", port=your-port)

Dependencies

  • httpx: HTTP client for file downloads and uploads
  • mcp[cli]: Model Context Protocol server framework
  • google-cloud-storage: Google Cloud Storage integration (optional)