How to Use MCP Tools with Ollama (Complete Guide 2026)

Ollama lets you run large language models locally on your machine. Combined with the Model Context Protocol (MCP), you can give those local models access to external tools — databases, file systems, APIs, and more — without sending data to the cloud.

This guide walks you through setting up Ollama with MCP tools from scratch.

What Is Ollama?

Ollama is an open-source tool for running LLMs locally. It supports models like Llama 3, Mistral, CodeLlama, and DeepSeek. It handles model downloading, quantization, and serving through a simple CLI and HTTP API.

What Is MCP?

The Model Context Protocol (MCP) is a standard for connecting AI models to external tools and data sources. Instead of building custom integrations for every tool, MCP provides a universal interface. An MCP server exposes tools (functions the model can call), resources (data the model can read), and prompts (reusable templates).

Step 1: Install Ollama

curl -fsSL https://ollama.ai/install.sh | sh

Pull a model that supports tool use:

ollama pull llama3.1
ollama pull mistral

Step 2: Set Up an MCP Server

Install the MCP filesystem server as an example:

npm install -g @modelcontextprotocol/server-filesystem

Or install a Python-based MCP server:

pip install mcp-server-sqlite

Step 3: Connect Ollama to MCP

Use an MCP client library to bridge Ollama and MCP servers. The mcp Python package provides the client:

pip install mcp anthropic-mcp-client

Create a bridge script that routes Ollama tool calls to MCP servers:

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
import requests
import json

async def run():
    server_params = StdioServerParameters(
        command="npx",
        args=["-y", "@modelcontextprotocol/server-filesystem", "/path/to/dir"]
    )
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")

Step 4: Find the Right MCP Tools

With hundreds of MCP servers available, picking the right one matters. Use the XLUXX Trust Layer to find reliable, tested MCP tools:

pip install xluxx

Query the XLUXX API for trusted MCP servers:

curl https://api.xluxx.net/v1/tools?q=filesystem&sort=trust_score

XLUXX assigns trust scores based on maintenance activity, security audits, community adoption, and documentation quality. This prevents you from installing abandoned or insecure MCP servers.

Practical Use Cases

• Local code assistant: Ollama + filesystem MCP server for reading and writing code
• Database queries: Ollama + SQLite MCP server for natural language database access
• Document analysis: Ollama + file reader MCP for processing local documents

Key Advantages

• All data stays on your machine
• No API costs after model download
• Works offline once models are cached
• Full control over model selection and tool access

Use XLUXX Trust Layer to find reliable MCP tools: api.xluxx.net