Ollama MCP Server

A lightweight Model Context Protocol (MCP) server that exposes Ollama Cloud models as tools to MCP clients such as Claude Code.

This server not only lets you chat with Ollama Cloud models — it also makes it easy to switch between the best model for each task: coding, math, reasoning, agentic workflows, and more.

Features

• List models – Discover all models available on your Ollama Cloud instance.
• Get / set default model – Switch the active model at runtime to match the task.
• Chat – Send prompts to any Ollama Cloud model, with optional system messages and per-request model overrides.
• Web search – Search the web through Ollama's hosted web search API.
• Web fetch – Fetch and extract a webpage's content through Ollama's hosted web fetch API.
• Task-aware model guide – Built-in recommendations for choosing the right model for coding, math, reasoning, and other workloads.

Requirements

• Python 3.10+
• An Ollama Cloud account with an API key.

Installation

1. Clone the repository:

   git clone https://github.com/chakkritt/ollama-mcp.git
   cd ollama-mcp
   

2. Create a virtual environment (recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

Configuration

Copy .env.example to .env and fill in your API key:

cp .env.example .env

Then edit .env and replace the placeholder values with your own:

OLLAMA_HOST=https://ollama.com
OLLAMA_API_KEY=YOUR_OLLAMA_API_KEY
DEFAULT_MODEL=gpt-oss:120b

• OLLAMA_HOST – Base URL of your Ollama Cloud instance.
• OLLAMA_API_KEY – Your Ollama Cloud API key.
• DEFAULT_MODEL – Fallback model used when a chat request does not specify one.

You can override the model on every chat request, so the DEFAULT_MODEL is just a sensible starting point.

Model selection guide

Use this guide to pick the best Ollama Cloud model for your task. Switch models anytime with the set_current_model tool.

Coding & software engineering

These models excel at writing, debugging, and reasoning about code.

Model	Why use it
glm-5.2	Flagship long-horizon model; great for complex coding and agentic engineering tasks.
kimi-k2.7-code	Coding-specialized variant of Kimi K2.6; efficient long-horizon coding with lower thinking-token usage.
kimi-k2.6	Native multimodal agentic model with strong long-horizon coding and autonomous execution.
minimax-m3	Coding and agentic frontier model with a 1M context window and native multimodality.

Recommended default for coding: glm-5.2 or kimi-k2.7-code.

Math, reasoning & problem solving

Model	Why use it
deepseek-v4-pro	Frontier MoE with multiple reasoning modes and a large context window; best for deep math and logic.
deepseek-v4-flash	Fast, efficient 284B MoE (13B activated) with 1M-token context and strong reasoning.
glm-5.2	Handles long-horizon reasoning tasks well.
nemotron-3-super	NVIDIA's efficient open MoE for complex multi-agent and reasoning applications.
nemotron-3-ultra	High-throughput reasoning for extended agent workflows.

Recommended default for math: deepseek-v4-pro.

General-purpose & agentic tasks

Model	Why use it
gpt-oss:120b	OpenAI open-weight model for reasoning, agentic tasks, and developer use cases.
mistral-large-3	Production-grade multimodal MoE for enterprise workloads.
qwen3.5	Large open-source multimodal family with sizes from 0.8B to 122B.
kimi-k2.5	Multimodal agentic model with vision/language understanding and instant/thinking modes.

Multimodal & vision

Model	Why use it
gemma4	Strong performance across scales; supports reasoning, coding, multimodal understanding, and audio.
gemini-3-flash-preview	Fast, cost-efficient frontier intelligence.
kimi-k2.6 / kimi-k2.5	Native multimodal agentic capabilities.
minimax-m3	Native multimodality with a 1M context window.

Fast / cost-efficient tasks

Model	Why use it
deepseek-v4-flash	Large MoE with only 13B activated parameters; fast and efficient.
gemini-3-flash-preview	Optimized for speed and cost.
nemotron-3-nano	Small, efficient agentic models at 4B and 30B.
qwen3.5:0.8b / qwen3.5:2b	Tiny, fast variants for simple tasks.

Running the server

python server.py

By default, the server runs as an MCP server over stdio, which is the standard transport for most MCP clients.

Available tools

Tool	Description
list_models	List all available Ollama Cloud models.
get_current_model	Return the current default model.
set_current_model	Change the default model used for chat.
chat	Send a prompt to the current (or specified) model. Supports an optional system message, a model override, and a think argument to enable extended thinking/reasoning mode (true or "low"/"medium"/"high"). When thinking is enabled, the response includes a thinking field with the model's reasoning trace.
web_search	Search the web using Ollama's hosted web search API. Returns up to max_results (default 5, max 10) results with title, URL, and a content snippet.
web_fetch	Fetch a webpage using Ollama's hosted web fetch API. Returns the page title, main text content, and up to 10 links.

Example usage with Claude Code

Add the server to your Claude Code MCP configuration:

{
  "mcpServers": {
    "ollama": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"],
      "env": {
        "OLLAMA_HOST": "https://ollama.com",
        "OLLAMA_API_KEY": "YOUR_OLLAMA_API_KEY",
        "DEFAULT_MODEL": "gpt-oss:120b"
      }
    }
  }
}

Then you can ask Claude to:

• "List available Ollama models"
• "Switch to the coding model" — the guide recommends glm-5.2 or kimi-k2.7-code.
• "Use the math model to solve this problem" — the guide recommends deepseek-v4-pro.
• "Chat with glm-5.2 and refactor this function"
• "Set the default model to deepseek-v4-pro"
• "Think through this problem with deepseek-v4-pro" — calls chat with think: true and returns the reasoning trace in thinking.
• "Search the web for the latest news on …" — uses web_search.
• "Fetch the page at https://… and summarize it" — uses web_fetch.

Quick task-to-model cheatsheet

Task	Suggested model
General coding	glm-5.2, kimi-k2.7-code
Complex / long-horizon coding	glm-5.2, kimi-k2.6, minimax-m3
Math & deep reasoning	deepseek-v4-pro, deepseek-v4-flash
Fast reasoning on a budget	deepseek-v4-flash, gemini-3-flash-preview
Agentic workflows	glm-5.2, minimax-m3, nemotron-3-super
Multimodal tasks	gemma4, kimi-k2.6, minimax-m3, qwen3.5
General-purpose chat	gpt-oss:120b, mistral-large-3, qwen3.5, gemma4

Project structure

ollama-mcp/
├── .env.example      # Environment variable template
├── .gitignore        # Files ignored by Git
├── LICENSE           # MIT license
├── pyproject.toml    # Project metadata
├── README.md         # This file
├── requirements.txt  # Python dependencies
└── server.py         # MCP server implementation

Dependencies

• fastmcp – Framework for building MCP servers in Python.
• ollama – Official Ollama Python client.
• httpx – HTTP client for the Ollama hosted web search/fetch APIs.
• python-dotenv – Load environment variables from .env.

Keeping model recommendations up to date

Ollama Cloud's model catalog changes frequently. To refresh this guide:

1. Visit https://ollama.com/search?c=cloud.
2. Update the Model selection guide and Quick task-to-model cheatsheet sections with new releases or benchmarks.
3. Adjust your DEFAULT_MODEL and set_current_model calls to match your current workload.

License

Apache License 2.0