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

Install dependencies:

pnpm install

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)