How to Connect Ollama to MCP (2026)

MCP (Model Context Protocol) lets AI models interact with external tools and data sources. Ollama lets you run powerful language models locally. Connecting the two gives you a locally-hosted AI assistant that can access files, query databases, call APIs, and use any MCP-compatible tool -- all without sending data to cloud providers.

This guide shows you how to bridge Ollama models with MCP servers, covering multiple approaches from simple to advanced.

Why Connect Ollama to MCP?

By default, Ollama models can only respond based on the conversation context you provide. They cannot access files, search the web, or interact with external systems. MCP changes this by giving the model access to tools it can call during a conversation.

Use cases for Ollama + MCP:

Local file access: Let the model read and search your codebase or documents
Database queries: The model can query SQLite, PostgreSQL, or other databases
API integration: Connect to GitHub, Jira, Slack, or any service with an MCP server
Web search: Add search capabilities to your local model
Complete privacy: All processing stays on your machine

Prerequisites

Requirement	Details
Ollama	Installed and running (ollama.com)
Node.js	v18+ (for MCP servers)
A model with tool support	Qwen 2.5, Llama 3.3, Hermes 3, or Mistral
RAM	8 GB minimum, 16+ GB recommended

Install Ollama (if needed)

# macOS/Linux
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model with good tool-calling support
ollama pull qwen2.5:7b

Verify Ollama is running:

curl http://localhost:11434/api/tags

Approach 1: MCP Client Bridge (Recommended)

The most practical approach is to use a bridge application that acts as an MCP client, connecting Ollama's API to MCP servers.

Step 1: Install the mcp-client-cli Bridge

npm install -g @anthropic/mcp-client-cli

This tool creates an interactive chat interface that connects an LLM to MCP servers.

Step 2: Create a Configuration File

Create mcp-config.json:

{
  "llm": {
    "provider": "openai-compatible",
    "baseUrl": "http://localhost:11434/v1",
    "apiKey": "ollama",
    "model": "qwen2.5:7b"
  },
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/yourname/projects"
      ]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Step 3: Start the Bridge

mcp-client-cli --config mcp-config.json

This opens an interactive chat where your Ollama model can use the configured MCP tools. Ask it to list files, read code, or interact with GitHub -- the model will call the appropriate MCP tools automatically.

Approach 2: Python Script with Tool Calling

For full control, build a Python script that handles the MCP protocol and routes tool calls to Ollama.

Step 1: Install Dependencies

pip install openai mcp

Step 2: Create the Bridge Script

import asyncio
import json
from openai import OpenAI
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Connect to Ollama
llm = OpenAI(
    api_key="ollama",
    base_url="http://localhost:11434/v1"
)

MODEL = "qwen2.5:7b"

async def run():
    # Start MCP server (filesystem access)
    server_params = StdioServerParameters(
        command="npx",
        args=["-y", "@modelcontextprotocol/server-filesystem", "/Users/yourname/projects"]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize MCP session
            await session.initialize()

            # Get available tools from MCP server
            tools_response = await session.list_tools()
            mcp_tools = tools_response.tools

            # Convert MCP tools to OpenAI function format
            openai_tools = []
            for tool in mcp_tools:
                openai_tools.append({
                    "type": "function",
                    "function": {
                        "name": tool.name,
                        "description": tool.description or "",
                        "parameters": tool.inputSchema if tool.inputSchema else {"type": "object", "properties": {}}
                    }
                })

            print(f"Connected to MCP server with {len(openai_tools)} tools")
            print("Tools:", [t["function"]["name"] for t in openai_tools])
            print("\nChat started. Type 'quit' to exit.\n")

            messages = []

            while True:
                user_input = input("You: ")
                if user_input.lower() in ("quit", "exit"):
                    break

                messages.append({"role": "user", "content": user_input})

                # Call Ollama with tools
                response = llm.chat.completions.create(
                    model=MODEL,
                    messages=messages,
                    tools=openai_tools if openai_tools else None,
                    tool_choice="auto"
                )

                assistant_message = response.choices[0].message

                # Handle tool calls
                if assistant_message.tool_calls:
                    messages.append(assistant_message)

                    for tool_call in assistant_message.tool_calls:
                        tool_name = tool_call.function.name
                        tool_args = json.loads(tool_call.function.arguments)

                        print(f"  [Calling tool: {tool_name}({tool_args})]")

                        # Execute via MCP
                        result = await session.call_tool(tool_name, tool_args)

                        # Add tool result to conversation
                        tool_result_text = ""
                        for content in result.content:
                            if hasattr(content, "text"):
                                tool_result_text += content.text

                        messages.append({
                            "role": "tool",
                            "tool_call_id": tool_call.id,
                            "content": tool_result_text
                        })

                    # Get final response after tool execution
                    final_response = llm.chat.completions.create(
                        model=MODEL,
                        messages=messages
                    )
                    final_text = final_response.choices[0].message.content
                    messages.append({"role": "assistant", "content": final_text})
                    print(f"AI: {final_text}\n")
                else:
                    content = assistant_message.content or ""
                    messages.append({"role": "assistant", "content": content})
                    print(f"AI: {content}\n")


asyncio.run(run())

Step 3: Run It

python ollama_mcp_bridge.py

Example session:

Connected to MCP server with 5 tools
Tools: ['read_file', 'write_file', 'list_directory', 'search_files', 'get_file_info']

You: List all TypeScript files in my projects directory
  [Calling tool: list_directory({"path": "/Users/yourname/projects"})]
  [Calling tool: search_files({"pattern": "*.ts", "path": "/Users/yourname/projects"})]
AI: I found 23 TypeScript files in your projects directory...

You: Read the main index.ts file and suggest improvements
  [Calling tool: read_file({"path": "/Users/yourname/projects/my-app/src/index.ts"})]
AI: Here are my suggestions for improving your index.ts...

Approach 3: Open WebUI with MCP Support

Open WebUI is a popular web interface for Ollama that has added MCP support. This gives you a ChatGPT-like interface powered by local models with MCP tool access.

Step 1: Install Open WebUI

docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

Step 2: Configure MCP Servers

In Open WebUI's admin settings:

Navigate to Admin > Settings > MCP
Add your MCP server configurations
Enable tool calling for your selected model

Step 3: Use It

Open http://localhost:3000 in your browser. Select an Ollama model that supports tool calling and start chatting. The model can now use the configured MCP tools through the web interface.

Best Models for MCP Tool Calling

Not all Ollama models handle tool calling well. Here are the most reliable options:

Model	Size (Q4)	Tool Calling	Reliability	Speed
Qwen 2.5 7B Instruct	4.5 GB	Excellent	High	Fast
Hermes 3 8B	5 GB	Excellent	High	Fast
Llama 3.3 8B Instruct	5 GB	Very Good	High	Fast
Mistral Small 24B	14 GB	Good	Medium	Medium
Qwen 2.5 72B Instruct	42 GB	Excellent	Very High	Slow
Command R+ 104B	60 GB	Very Good	High	Slow

Recommendation: Start with qwen2.5:7b for the best balance of tool-calling reliability and performance. Upgrade to qwen2.5:72b if you have the VRAM for it.

# Pull the recommended model
ollama pull qwen2.5:7b

# Or for better quality with more VRAM
ollama pull qwen2.5:72b-instruct-q4_K_M

Useful MCP Servers to Connect

Here are the most practical MCP servers to pair with Ollama:

MCP Server	Package	Use Case
Filesystem	`@modelcontextprotocol/server-filesystem`	Read/write local files
GitHub	`@modelcontextprotocol/server-github`	Issues, PRs, repos
SQLite	`@modelcontextprotocol/server-sqlite`	Query local databases
Brave Search	`@anthropic/mcp-server-brave-search`	Web search
Fetch	`@modelcontextprotocol/server-fetch`	Fetch web page content
Memory	`@modelcontextprotocol/server-memory`	Persistent memory across sessions

Install and use any of them with:

npx -y @modelcontextprotocol/server-filesystem /path/to/directory

Troubleshooting

"Model does not support tool calling" Not all Ollama models support the tool-calling format. Use Qwen 2.5, Hermes 3, or Llama 3.3 Instruct. Avoid base models and models without "instruct" in the name.

Tool calls return malformed JSON Lower the temperature to 0.1 in your Ollama configuration. Higher temperatures cause the model to generate invalid JSON for tool call arguments.

response = llm.chat.completions.create(
    model=MODEL,
    messages=messages,
    tools=openai_tools,
    temperature=0.1  # Low temperature for reliable tool calling
)

MCP server fails to start Make sure Node.js v18+ is installed and the MCP server package installs correctly. Test by running the npx command manually in your terminal.

Ollama not responding on port 11434 Start the Ollama server with:

ollama serve

Or check if it is running:

curl http://localhost:11434/api/tags

Slow responses with tool calling Tool calling adds latency because the model generates a tool call, the tool executes, and then the model processes the result. Use a smaller, faster model (7B) and ensure your GPU is being utilized. Check with nvidia-smi or ollama ps.

Wrapping Up

Connecting Ollama to MCP gives you a completely local, privacy-preserving AI assistant that can interact with your files, databases, and services. The setup ranges from a 5-minute configuration with a pre-built bridge to a fully custom Python integration. The key to success is choosing a model with strong tool-calling support and keeping inference parameters conservative.

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