ray-mcp-server

# ⚡ Ray MCP Server 🚀 **Supercharge your AI workflows with distributed computing!** A powerful Model Context Protocol (MCP) server that brings Ray's distributed computing capabilities directly to Claude Desktop. Manage clusters, submit jobs, and orchestrate complex workflows through natural language commands! 🎯 ## ✨ Features - 🏗️ **Multi-Node Cluster Management**: Start and manage Ray clusters with head nodes and worker nodes - 🚀 **Job Management**: Submit, monitor, and manage Ray jobs with ease - 🎭 **Actor Management**: Create and manage Ray actors for stateful computations - 📊 **Real-time Monitoring**: Get cluster status, resource usage, and performance metrics - 🔍 **Logging and Debugging**: Access logs and debug job issues seamlessly - ⏰ **Scheduling**: Schedule jobs with cron-like syntax for automated workflows ## 🚀 Quick Start ### 📦 Installation **🚀 Super Easy with uvx (Recommended):** ```bash # Install and run directly with uvx - no setup needed! ⚡ uvx ray-mcp-server ``` **📥 Or clone for development:** ```bash # Clone the repository git clone <repository-url> cd ray-mcp # Install dependencies with UV (lightning fast! ⚡) uv sync # You're ready to go! 🎉 ``` ### 🎯 Starting Ray Clusters The server supports both single-node and multi-node cluster configurations: #### 🖥️ Simple Single-Node Cluster ```json { "tool": "start_ray", "arguments": { "num_cpus": 4, "num_gpus": 1 } } ``` #### 🌐 Multi-Node Cluster (Default) The server now defaults to starting multi-node clusters with 2 worker nodes (perfect for scaling! 📈): ```json { "tool": "start_ray", "arguments": { "num_cpus": 1 } } ``` This creates: - 🧠 **Head node**: 1 CPU, 0 GPUs, 1GB object store memory - ⚙️ **Worker node 1**: 2 CPUs, 0 GPUs, 500MB object store memory - ⚙️ **Worker node 2**: 2 CPUs, 0 GPUs, 500MB object store memory #### 🛠️ Custom Multi-Node Setup For advanced configurations, you can specify custom worker nodes: ```json { "tool": "sta…