MCP + Ollama Local Tool Calling Example

Name: MCP + Ollama Local Tool Calling Example
Author: rajeevchandra

@rajeevchandra

Repository

About MCP + Ollama Local Tool Calling Example

No overview available yet

#mcp #ollama #local-llm #function-calling #python #ai #developer-tools #autonomous-agents

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-client-server-example": {
      "command": "python",
      "args": [
        "math_server.py"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP + Ollama Local Tool Calling Example?

It is a demonstration project that shows how a local AI agent can understand user queries and automatically call Python functions using the Model Context Protocol (MCP), a local LLM via Ollama, and a Python MCP client and server.

How to use MCP + Ollama Local Tool Calling Example?

Install dependencies with pip install "mcp[cli] @ git+https://github.com/awslabs/mcp.git" openai==0.28 httpx, ensure Ollama is installed and running, pull a tool-calling‑capable model (e.g., ollama run llama3), run the MCP server (python math_server.py), then start the client with python ollama_client.py math_server.py. Finally, type queries like “What is 5 + 8?” and the system will respond with the computed result.

Key features of MCP + Ollama Local Tool Calling Example

Uses MCP to describe tools for the LLM
Runs a local LLM (e.g., Llama3) via Ollama
Python functions (add, multiply) become callable tools
Fully autonomous – no manual tool selection
Everything runs offline and locally

Use cases of MCP + Ollama Local Tool Calling Example

Building a local AI developer assistant that can perform calculations
Creating secure, offline‑only AI workflows for sensitive data
Prototyping autonomous agents that reason about and invoke functions

FAQ from MCP + Ollama Local Tool Calling Example

What does this project demonstrate?

It demonstrates how an LLM can automatically decide which Python function to call based on a user’s intent, using MCP to describe available tools and Ollama to run the model locally.

What are the runtime requirements?

You need Python, the mcp[cli], openai==0.28, and httpx packages, plus Ollama installed and running with a model that supports tool calling (e.g., Llama3).

Does the system require an internet connection?

No – everything runs locally. The LLM is served by Ollama on your machine, and the MCP components communicate over local transports.

Which tools are exposed by the MCP server?

The example server (math_server.py) exposes two tools: add(a: int, b: int) -> int and multiply(a: int, b: int) -> int.

Can I use a different LLM model?

Yes, as long as the model supports tool‑calling. The README suggests Llama3 via the ollama run llama3 command; you can replace it with any compatible Ollama model.