Ollama MCP Server

🚀 A powerful bridge between Ollama and the Model Context Protocol (MCP), enabling seamless integration of Ollama's local LLM capabilities into your MCP-powered applications.

🌟 Features

Complete Ollama Integration

• Full API Coverage: Access all essential Ollama functionality through a clean MCP interface
• OpenAI-Compatible Chat: Drop-in replacement for OpenAI's chat completion API
• Local LLM Power: Run AI models locally with full control and privacy

Core Capabilities

• 🔄 Model Management
  • Pull models from registries
  • Push models to registries
  • List available models
  • Create custom models from Modelfiles
  • Copy and remove models

• 🤖 Model Execution
  • Run models with customizable prompts
  • Chat completion API with system/user/assistant roles
  • Configurable parameters (temperature, timeout)
  • Raw mode support for direct responses

• 🛠 Server Control
  • Start and manage Ollama server
  • View detailed model information
  • Error handling and timeout management

🚀 Getting Started

Prerequisites

• Ollama installed on your system
• Node.js and npm/pnpm

Installation

1. Install dependencies:

pnpm install

2. Build the server:

pnpm run build

Configuration

Add the server to your MCP configuration:

For Claude Desktop:

MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`

Windows: `%APPDATA%/Claude/claude_desktop_config.json`

{
  "mcpServers": {
    "ollama": {
      "command": "node",
      "args": ["/path/to/ollama-server/build/index.js"],
      "env": {
        "OLLAMA_HOST": "http://127.0.0.1:11434"  // Optional: customize Ollama API endpoint
      }
    }
  }
}

🛠 Usage Examples

Pull and Run a Model

// Pull a model
await mcp.use_mcp_tool({
  server_name: "ollama",
  tool_name: "pull",
  arguments: {
    name: "llama2"
  }
});

// Run the model
await mcp.use_mcp_tool({
  server_name: "ollama",
  tool_name: "run",
  arguments: {
    name: "llama2",
    prompt: "Explain quantum computing in simple terms"
  }
});

Chat Completion (OpenAI-compatible)

await mcp.use_mcp_tool({
  server_name: "ollama",
  tool_name: "chat_completion",
  arguments: {
    model: "llama2",
    messages: [
      {
        role: "system",
        content: "You are a helpful assistant."
      },
      {
        role: "user",
        content: "What is the meaning of life?"
      }
    ],
    temperature: 0.7
  }
});

Create Custom Model

await mcp.use_mcp_tool({
  server_name: "ollama",
  tool_name: "create",
  arguments: {
    name: "custom-model",
    modelfile: "./path/to/Modelfile"
  }
});

🔧 Advanced Configuration

• `OLLAMA_HOST`: Configure custom Ollama API endpoint (default: http://127.0.0.1:11434)
• Timeout settings for model execution (default: 60 seconds)
• Temperature control for response randomness (0-2 range)

🤝 Contributing

Contributions are welcome! Feel free to:

• Report bugs
• Suggest new features
• Submit pull requests

📝 License

MIT License - feel free to use in your own projects!

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