Rendi MCP Server

# Rendi MCP Server A Model Context Protocol (MCP) server that provides cloud-based FFmpeg processing capabilities through the [Rendi API](https://rendi.dev). 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](https://rendi.dev) 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](https://rendi.dev)) - An MCP-compatible client (Claude Desktop, Cline, etc.) ## Installation ### Via Smithery The easiest way to install this server is through [Smithery](https://smithery.ai): ```bash 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: ```bash git clone https://github.com/ctaylor86/rendi-mcp-server.git cd rendi-mcp-server ``` 2. Install dependencies: ```bash npm install ``` 3. Build the project: ```bash npm run build ``` 4. 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`: ```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 ```bash npm run dev ``` ### Build ```bash npm run build ``` ### Start Production Server ```bash 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: - [API Introduction](https://docs.rendi.dev/api-reference/introduction) - [Run FFmpeg Command](https://docs.rendi.dev/api-reference/endpoint/run-ffmpeg-command) - [Run Chained Commands](https://docs.rendi.dev/api-reference/endpoint/run-chained-ffmpeg-commands) - [Poll Command](https://docs.rendi.dev/api-reference/endpoint/poll-command) - [Delete Files](https://docs.rendi.dev/api-reference/endpoint/delete-command-files) ## License MIT ## Support For issues with this MCP server, please open an issue on GitHub. For Rendi API support, visit [rendi.dev](https://rendi.dev) or check their [documentation](https://docs.rendi.dev). ## Contributing Contributions are welcome! Please feel free to submit a Pull Request.