# MCP Ollama Server

A Model Context Protocol (MCP) server that provides direct access to Ollama models for AI inference.

## Features

- 🚀 **Direct Model Access**: Generate responses and chat with any Ollama model
- 💬 **Chat Support**: Maintain conversation context with chat endpoints
- 📋 **Model Management**: List, pull, delete, and get info about models
- 🔢 **Embeddings**: Generate text embeddings for semantic search
- 🔧 **Full Control**: Configure temperature, max tokens, and system prompts
- ✅ **Status Checking**: Automatic Ollama availability detection

## Installation

1. **Prerequisites**:
   - [Ollama](https://ollama.ai) installed and running
   - Node.js 18+ installed

2. **Install the MCP server**:
   ```bash
   cd /Users/bard/Code/mcp-ollama
   npm install
   npm run build
   ```

3. **Add to Claude Desktop config**:
   Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:
   ```json
   {
     "mcpServers": {
       "ollama": {
         "command": "node",
         "args": ["/Users/bard/Code/mcp-ollama/dist/index.js"],
         "env": {
           "OLLAMA_BASE_URL": "http://localhost:11434"
         }
       }
     }
   }
   ```

4. **Restart Claude Desktop**

## Usage

### Generate Text
```javascript
// Simple generation
ollama_generate({
  prompt: "What is the meaning of life?"
})

// With system prompt and parameters
ollama_generate({
  model: "llama3.2",
  prompt: "Write a haiku about coding",
  system: "You are a creative poet",
  temperature: 0.9,
  max_tokens: 100
})
```

### Chat Conversations
```javascript
// Multi-turn conversation
ollama_chat({
  model: "llama3.2",
  messages: [
    { role: "system", content: "You are a helpful assistant" },
    { role: "user", content: "What is Python?" },
    { role: "assistant", content: "Python is a high-level programming language..." },
    { role: "user", content: "What makes it good for beginners?" }
  ]
})
```

### Model Management
```javascript
// List available models
ollama_list()

// Pull a new model
ollama_pull({ model: "mistral" })

// Get model information
ollama_info({ model: "llama3.2" })

// Delete a model
ollama_delete({ model: "old-model" })
```

### Generate Embeddings
```javascript
// Generate embeddings for semantic search
ollama_embeddings({
  model: "nomic-embed-text",
  prompt: "The quick brown fox jumps over the lazy dog"
})
```

## Available Models

Popular models you can use:
- `llama3.2` - Fast, efficient general-purpose model
- `deepseek-r1` - Advanced reasoning model
- `mistral` - Efficient 7B parameter model
- `gemma:2b` - Google's small efficient model
- `phi3:mini` - Microsoft's compact model
- `nomic-embed-text` - For generating embeddings

Pull any model with:
```bash
ollama pull <model-name>
```

## Configuration

### Environment Variables

- `OLLAMA_BASE_URL`: Ollama API endpoint (default: `http://localhost:11434`)

### Tool Parameters

#### ollama_generate
- `model`: Model to use (default: "llama3.2")
- `prompt`: Input prompt (required)
- `system`: System prompt (optional)
- `temperature`: Sampling temperature 0-1 (default: 0.7)
- `max_tokens`: Maximum tokens to generate (default: 2048)
- `stream`: Stream responses (default: false)

#### ollama_chat
- `model`: Model to use (default: "llama3.2")
- `messages`: Array of chat messages (required)
- `temperature`: Sampling temperature 0-1 (default: 0.7)
- `max_tokens`: Maximum tokens to generate (default: 2048)

## Troubleshooting

### Ollama not running
If you see "❌ Ollama is not running", start Ollama:
```bash
ollama serve
```

### No models available
Pull a model first:
```bash
ollama pull llama3.2
```

### Different Ollama port
If Ollama runs on a different port, update the config:
```json
{
  "env": {
    "OLLAMA_BASE_URL": "http://localhost:YOUR_PORT"
  }
}
```

## Differences from ELVIS

This MCP server provides direct, synchronous access to Ollama models, unlike ELVIS which uses a delegation/queue pattern. Benefits:

- **Immediate responses**: No waiting for task completion
- **Simpler API**: Direct function calls instead of task management
- **Native chat support**: Built-in conversation handling
- **Model management**: Pull, delete, and inspect models
- **Embeddings support**: Generate embeddings for RAG applications

## Development

### Running in development:
```bash
npm run dev
```

### Building:
```bash
npm run build
```

### Testing:
```bash
# Test generate
curl -X POST http://localhost:11434/api/generate \
  -d '{"model": "llama3.2", "prompt": "Hello"}'

# Test chat
curl -X POST http://localhost:11434/api/chat \
  -d '{"model": "llama3.2", "messages": [{"role": "user", "content": "Hello"}]}'
```

## License

MIT