Rendi is a cloud-based FFmpeg processing service that enables video and audio conversions, editing, and workflows without local installation. It provides cloud infrastructure for running FFmpeg commands through the Rendi API. Key features of Rendi: - Execute single FFmpeg commands for video/audio conversions, resizing, and format changes - Chain multiple sequential commands for complex workflows (e.g., convert → extract thumbnail → apply watermark) - Poll commands to check processing status, execution metrics, and retrieve output metadata - Direct download URLs for processed files with codec, resolution, duration, and bitrate details - Delete output files to manage cloud storage space

Rendi MCP Server

A Model Context Protocol (MCP) server that provides cloud-based FFmpeg processing capabilities through the Rendi API. This server enables AI assistants to run FFmpeg commands in the cloud without local installation or infrastructure.

Features

This MCP server provides four powerful tools for cloud-based video and audio processing:

🎬 Run FFmpeg Command

⛓️ Run Chained FFmpeg Commands

Chained commands are more efficient than running commands separately as they share system resources.

📊 Poll FFmpeg Command

🗑️ Delete Command Files

What is Rendi?

Rendi is a cloud-based FFmpeg API service that allows you to run FFmpeg commands without installing FFmpeg locally. It provides:

Prerequisites

Installation

Via Smithery

The easiest way to install this server is through Smithery:

npx @smithery/cli install rendi-mcp-server

You'll be prompted to enter your Rendi API key during installation.

Manual Installation

1. Clone this repository:

git clone https://github.com/ctaylor86/rendi-mcp-server.git
cd rendi-mcp-server

2. Install dependencies:

npm install

3. Build the project:

npm run build

4. Configure your MCP client to use this server with your Rendi API key.

Configuration

This server requires one configuration parameter:

Example Configuration for Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "rendi": {
      "command": "node",
      "args": ["/path/to/rendi-mcp-server/dist/index.js"],
      "env": {
        "RENDI_API_KEY": "your-rendi-api-key-here"
      }
    }
  }
}

Usage Examples

Simple Video Conversion

Use the run_ffmpeg_command tool to convert a video to MP4:
Command: "-i {{in_1}} -c:v libx264 -c:a aac {{out_1}}"
Input files: {"in_1": "https://example.com/video.avi"}
Output files: {"out_1": "converted.mp4"}

Extract Thumbnail from Video

Use the run_ffmpeg_command tool to extract a thumbnail:
Command: "-i {{in_1}} -ss 00:00:05 -vframes 1 {{out_1}}"
Input files: {"in_1": "https://example.com/video.mp4"}
Output files: {"out_1": "thumbnail.jpg"}

Complex Workflow with Chained Commands

Use the run_chained_ffmpeg_commands tool for a multi-step workflow:
1. Concatenate two videos
2. Extract a thumbnail from the result
Commands:
[
  "-i {{in_1}} -i {{in_2}} -filter_complex \"[0:v][1:v]concat=n=2:v=1:a=0[v]\" -map [v] {{out_1}}",
  "-i {{out_1}} -ss 00:00:10 -vframes 1 {{out_2}}"
]
Input files: {
  "in_1": "https://example.com/part1.mp4",
  "in_2": "https://example.com/part2.mp4"
}
Output files: {
  "out_1": "concatenated.mp4",
  "out_2": "thumbnail.jpg"
}

Check Command Status

After submitting a command, use poll_ffmpeg_command with the returned command_id to check status and get results.

Clean Up Files

When you're done with the output files, use delete_command_files with the command_id to free up storage.

Important Notes

File Naming Convention

Rendi uses a specific aliasing system for files:

Input File Requirements

Output File Storage