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Ollama MCP Server

Ollama MCP Server

Local
github.com/rawvegAI & Agents176Updated 11w ago

About Ollama MCP Server

Ollama MCP Server is an MCP server that exposes the Ollama SDK as MCP tools. It suits users who want local LLM access from clients such as Claude Desktop or Cline, with optional Ollama Cloud web search and fetch.

Key features

  1. 0114 MCP tools covering model management and operations
  2. 02Hybrid use of local models and Ollama Cloud
  3. 03Web search and web fetch tools via Ollama Cloud
  4. 04TypeScript and Zod based input validation
  5. 05Retry handling for rate limits and transient 5xx errors

Use cases

  1. 01Chatting with a local Ollama model from Claude Desktop
  2. 02Generating text embeddings with ollama_embed
  3. 03Using cloud web search from Cline during development

MCP support

This is an MCP server that exposes Ollama model listing, chat, generation, embedding and web search operations as tools to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"ollama": {
"command": "npx",
"args": [
"-y",
"ollama-mcp"
]
}
}
}
Run commandnpx -y ollama-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmollama-mcp

Tools

13

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
modelstringYesName of the model to use
messagesarrayYesArray of chat messages
toolsstringNoTools that the model can call (optional). Provide as JSON array of tool objects.
optionsstringNoGeneration options (optional). Provide as JSON object with settings like temperature, top_p, etc.
formatenumNojson | markdown

Details

From the project README

🦙 Ollama MCP Server

Supercharge your AI assistant with local LLM access

License: AGPL-3.0 TypeScript MCP Coverage

An MCP (Model Context Protocol) server that exposes the complete Ollama SDK as MCP tools, enabling seamless integration between your local LLM models and MCP-compatible applications like Claude Desktop and Cline.

Features • Installation • Available Tools • Configuration • Retry Behavior • Development


✨ Features

  • ☁️ Ollama Cloud Support - Full integration with Ollama's cloud platform
  • 🔧 14 Comprehensive Tools - Full access to Ollama's SDK functionality
  • 🔄 Hot-Swap Architecture - Automatic tool discovery with zero-config
  • 🎯 Type-Safe - Built with TypeScript and Zod validation
  • 📊 High Test Coverage - 96%+ coverage with comprehensive test suite
  • 🚀 Zero Dependencies - Minimal footprint, maximum performance
  • 🔌 Drop-in Integration - Works with Claude Desktop, Cline, and other MCP clients
  • 🌐 Web Search & Fetch - Real-time web search and content extraction via Ollama Cloud
  • 🔀 Hybrid Mode - Use local and cloud models seamlessly in one server

💡 Level Up Your Ollama Experience with Claude Code and Desktop

The Complete Package: Tools + Knowledge

This MCP server gives Claude the tools to interact with Ollama - but you'll get even more value by also installing the Ollama Skill from the Skillsforge Marketplace:

  • 🚗 This MCP = The Car - All the tools and capabilities
  • 🎓 Ollama Skill = Driving Lessons - Expert knowledge on how to use them effectively

The Ollama Skill teaches Claude:

  • Best practices for model selection and configuration
  • Optimal prompting strategies for different Ollama models
  • When to use chat vs generate, embeddings, and other tools
  • Performance optimization and troubleshooting
  • Advanced features like tool calling and function support

Install both for the complete experience:

  1. ✅ This MCP server (tools)
  2. ✅ Ollama Skill (expertise)

Result: Claude doesn't just have the car - it knows how to drive! 🏎️

📦 Installation

Quick Start with Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "ollama": {
      "command": "npx",
      "args": ["-y", "ollama-mcp"]
    }
  }
}
Global Installation
npm install -g ollama-mcp
For Cline (VS Code)

Add to your Cline MCP settings (cline_mcp_settings.json):

{
  "mcpServers": {
    "ollama": {
      "command": "npx",
      "args": ["-y", "ollama-mcp"]
    }
  }
}

🛠️ Available Tools

Model Management
ToolDescription
ollama_listList all available local models
ollama_showGet detailed information about a specific model
ollama_pullDownload models from Ollama library
ollama_pushPush models to Ollama library
ollama_copyCreate a copy of an existing model
ollama_deleteRemove models from local storage
ollama_createCreate custom models from Modelfile
Model Operations
ToolDescription
ollama_psList currently running models
ollama_generateGenerate text completions
ollama_chatInteractive chat with models (supports tools/functions)
ollama_embedGenerate embeddings for text
Web Tools (Ollama Cloud)
ToolDescription
ollama_web_searchSearch the web with customizable result limits (requires OLLAMA_API_KEY)
ollama_web_fetchFetch and parse web page content (requires OLLAMA_API_KEY)

Note: Web tools require an Ollama Cloud API key. They connect to https://ollama.com/api for web search and fetch operations.

⚙️ Configuration

Environment Variables
VariableDefaultDescription
OLLAMA_HOSThttp://127.0.0.1:11434Ollama server endpoint (use https://ollama.com for cloud)
OLLAMA_API_KEY-API key for Ollama Cloud (required for web tools and cloud models)
Custom Ollama Host
{
  "mcpServers": {
    "ollama": {
      "command": "npx",
      "args": ["-y", "ollama-mcp"],
      "env": {
        "OLLAMA_HOST": "http://localhost:11434"
      }
    }
  }
}
Ollama Cloud Configuration

To use Ollama's cloud platform with web search and fetch capabilities:

{
  "mcpServers": {
    "ollama": {
      "command": "npx",
      "args": ["-y", "ollama-mcp"],
      "env": {
        "OLLAMA_HOST": "https://ollama.com",
        "OLLAMA_API_KEY": "your-ollama-cloud-api-key"
      }
    }
  }
}

Cloud Features:

  • ☁️ Access cloud-hosted models
  • 🔍 Web search with ollama_web_search (requires API key)
  • 📄 Web fetch with ollama_web_fetch (requires API key)
  • 🚀 Faster inference on cloud infrastructure

Get your API key: Visit ollama.com to sign up and obtain your API key.

Hybrid Mode (Local + Cloud)

You can use both local and cloud models by pointing to your local Ollama instance while providing an API key:

{
  "mcpServers": {
    "ollama": {
      "command": "npx",
      "args": ["-y", "ollama-mcp"],
      "env": {
        "OLLAMA_HOST": "http://127.0.0.1:11434",
        "OLLAMA_API_KEY": "your-ollama-cloud-api-key"
      }
    }
  }
}

This configuration:

  • ✅ Runs local models from your Ollama instance
  • ✅ Enables cloud-only web search and fetch tools
  • ✅ Best of both worlds: privacy + web connectivity

🔄 Retry Behavior

The MCP server includes intelligent retry logic for handling transient failures when communicating with Ollama APIs:

Automatic Retry Strategy

Web Tools (ollama_web_search and ollama_web_fetch):

  • Automatically retry on rate limit errors (HTTP 429)
  • Maximum of 3 retry attempts (4 total requests including initial)
  • Request timeout: 30 seconds per request (prevents hung connections)
  • Respects the Retry-After header when provided by the API
  • Falls back to exponential backoff with jitter when Retry-After is not present
Retry-After Header Support

The server intelligently handles the standard HTTP Retry-After header in two formats:

1. Delay-Seconds Format:

Retry-After: 60

Waits exactly 60 seconds before retrying.

2. HTTP-Date Format:

Retry-After: Wed, 21 Oct 2025 07:28:00 GMT

Calculates delay until the specified timestamp.

Exponential Backoff

When Retry-After is not provided or invalid:

  • Initial delay: 1 second (default)
  • Maximum delay: 10 seconds (default, configurable)
  • Strategy: Exponential backoff with full jitter
  • Formula: random(0, min(initialDelay × 2^attempt, maxDelay))

Example retry delays:

  • 1st retry: 0-1 seconds
  • 2nd retry: 0-2 seconds
  • 3rd retry: 0-4 seconds (capped at 0-10s max)
Error Handling

Retried Errors (transient failures):

  • HTTP 429 (Too Many Requests) - rate limiting
  • HTTP 500 (Internal Server Error) - transient server issues
  • HTTP 502 (Bad Gateway) - gateway/proxy received invalid response
  • HTTP 503 (Service Unavailable) - server temporarily unable to handle request
  • HTTP 504 (Gateway Timeout) - gateway/proxy did not receive timely response

Non-Retried Errors (permanent failures):

  • Request timeouts (30 second limit exceeded)
  • Network timeouts (no status code)
  • Abort/cancel errors
  • HTTP 4xx errors (except 429) - client errors requiring changes
  • Other HTTP 5xx errors (501, 505, 506, 508, etc.) - configuration/implementation issues

The retry mechanism ensures robust handling of temporary API issues while respecting server-provided retry guidance and preventing excessive request rates. Transient 5xx errors (500, 502, 503, 504) are safe to retry for the idempotent POST operations used by ollama_web_search and ollama_web_fetch. Individual requests timeout after 30 seconds to prevent indefinitely hung connections.

🎯 Usage Examples

Chat with a Model
// MCP clients can invoke:
{
  "tool": "ollama_chat",
  "arguments": {
    "model": "llama3.2:latest",
    "messages": [
      { "role": "user", "content": "Explain quantum computing" }
    ]
  }
}
Generate Embeddings
{
  "tool": "ollama_embed",
  "arguments": {
    "model": "nomic-embed-text",
    "input": ["Hello world", "Embeddings are great"]
  }
}
Web Search
{
  "tool": "ollama_web_search",
  "arguments": {
    "query": "latest AI developments",
    "max_results": 5
  }
}

🏗️ Architecture

This server uses a hot-swap autoloader pattern:

src/
├── index.ts          # Entry point (27 lines)
├── server.ts         # MCP server creation
├── autoloader.ts     # Dynamic tool discovery
└── tools/            # Tool implementations
    ├── chat.ts       # Each exports toolDefinition
    ├── generate.ts
    └── ...

Key Benefits:

  • Add new tools by dropping files in src/tools/
  • Zero server code changes required
  • Each tool is independently testable
  • 100% function coverage on all tools

🧪 Development

Prerequisites
  • Node.js v16+
  • npm or pnpm
  • Ollama running locally
Setup
# Clone repository
git clone https://github.com/rawveg/ollama-mcp.git
cd ollama-mcp

# Install dependencies
npm install

# Build project
npm run build

# Run tests
npm test

# Run tests with coverage
npm run test:coverage
Test Coverage
Statements   : 96.37%
Branches     : 84.82%
Functions    : 100%
Lines        : 96.37%
Adding a New Tool
  1. Create src/tools/your-tool.ts:
import { ToolDefinition } from '../autoloader.js';
import { Ollama } from 'ollama';
import { ResponseFormat } from '../types.js';

export const toolDefinition: ToolDefinition = {
  name: 'ollama_your_tool',
  description: 'Your tool description',
  inputSchema: {
    type: 'object',
    properties: {
      param: { type: 'string' }
    },
    required: ['param']
  },
  handler: async (ollama, args, format) => {
    // Implementation
    return 'result';
  }
};
  1. Create tests in tests/tools/your-tool.test.ts
  2. Done! The autoloader discovers it automatically.

🤝 Contributing

Contributions are welcome! Please follow these guidelines:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Write tests - We maintain 96%+ coverage
  4. Commit with clear messages (git commit -m 'Add amazing feature')
  5. Push to your branch (git push origin feature/amazing-feature)
  6. Open a Pull Request
Code Quality Standards
  • All new tools must export toolDefinition
  • Maintain ≥80% test coverage
  • Follow existing TypeScript patterns
  • Use Zod schemas for input validation

📄 License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).

See LICENSE for details.

🔗 Related Projects

🙏 Acknowledgments

Built with:

  • Ollama SDK - Official Ollama JavaScript library
  • MCP SDK - Model Context Protocol SDK
  • Zod - TypeScript-first schema validation

⬆ back to top

Made with ❤️ by Tim Green

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.