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
Execute a single FFmpeg command in the cloud with automatic file handling and processing. Perfect for simple video conversions, resizing, format changes, and basic editing tasks.
⛓️ Run Chained FFmpeg Commands
Submit multiple sequential FFmpeg commands where outputs from earlier commands can be used as inputs in later ones. This is ideal for complex workflows like:
- Convert video → Extract thumbnail → Apply watermark
- Trim video → Resize → Extract audio → Convert to MP3
Chained commands are more efficient than running commands separately as they share system resources.
📊 Poll FFmpeg Command
Check the status of submitted commands and retrieve results including:
- Processing status (queued, processing, success, failed)
- Execution time and performance metrics
- Output file metadata (resolution, duration, bitrate, codec, etc.)
- Direct download URLs for processed files
🗑️ Delete Command Files
Clean up output files from Rendi's storage when you're done with them. This helps manage storage space and keeps your workspace organized.
What is Rendi?
Rendi is a cloud-based FFmpeg API service that allows you to run FFmpeg commands without installing FFmpeg locally. It provides:
- ☁️ Cloud-based processing infrastructure
- 🚀 Scalable vCPU allocation (up to your account limit)
- 📦 Automatic file storage and management
- 🔒 Secure API key authentication
- ⚡ Fast processing with configurable resources
Prerequisites
- A Rendi API key (get one at rendi.dev)
- An MCP-compatible client (Claude Desktop, Cline, etc.)
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
- Clone this repository:
git clone https://github.com/ctaylor86/rendi-mcp-server.git
cd rendi-mcp-server
- Install dependencies:
npm install
- Build the project:
npm run build
- Configure your MCP client to use this server with your Rendi API key.
Configuration
This server requires one configuration parameter:
- rendiApiKey (required): Your Rendi API key for authentication
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 files: Must use keys starting with
in_(e.g.,in_1,in_video,in_audio) - Output files: Must use keys starting with
out_(e.g.,out_1,out_result,out_thumbnail) - In commands: Reference files using
{{alias}}format (e.g.,{{in_1}},{{out_1}})
Input File Requirements
- Input files must be publicly accessible URLs
- Supported sources: Direct URLs, Google Drive, Dropbox, S3, Rendi storage, etc.
- The filename should appear at the end of the URL
Output File Storage
- Output files are stored indefinitely on Rendi's servers until you delete them
- Each output file includes a direct download URL
- Use the delete_command_files tool to clean up when done
Processing Limits
- Maximum 10 commands per chain
- Default timeout: 300 seconds per command (configurable)
- Default vCPUs: 8 (configurable up to your account limit)
Development
Run in Development Mode
npm run dev
Build
npm run build
Start Production Server
npm start
Architecture
This server is built using:
- TypeScript for type-safe development
- Express for HTTP server functionality
- @modelcontextprotocol/sdk for MCP protocol implementation
- @smithery/sdk for Smithery integration
- Zod for schema validation
- Docker for containerized deployment
API Reference
For detailed information about the Rendi API, visit:
License
MIT
Support
For issues with this MCP server, please open an issue on GitHub.
For Rendi API support, visit rendi.dev or check their documentation.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.