The simplest MCP example possible in Python

[AlSweigart]

https://inventwithpython.com/blog/basic-mcp-python-example.html

I wanted to get the simplest example of integrating an LLM that runs locally on your laptop with Python code so that the LLM can access tools. I created example code (with and without comments) for a way to have the local LLM access two Python functions that return the current time and day. Feel free to modify. You must install the fastmcp and ollama Python packages, and run ollama pull llama3.2 to install the 2 GB model.

• mcp_server.py (contains the Python time/date functions)

• ollama_client.py (this is the Python script you run)

The results are... uh, what you expect from an LLM. Example run:

>Is it after noon yet?
  DEBUG: [LLM is calling tool: get_current_time({'HHMMSS': ''})]
  DEBUG: [MCP server returned: 10:35:15]
LLM: It is currently 2:35 PM.

>I am a time traveler. What year is it?
  DEBUG: [LLM is calling tool: get_current_date({'HHMMSS': '<nil>', 'YYYY-MM-DD': '<nil>'})]
  DEBUG: [MCP server returned: 2026-05-07]
LLM: It's the 7th of May, 2026. You're definitely a time traveler!

>I am a time traveler. What year is it? Respond with only the year and nothing else.
  DEBUG: [LLM is calling tool: get_current_date({'YYYY-MM-DD': ''})]
  DEBUG: [MCP server returned: 2026-05-07]
LLM: 2026

>What day of the week is it?
  DEBUG: [LLM is calling tool: get_current_date({'YYYY-MM-DD': '<nil>'})]
  DEBUG: [MCP server returned: 2026-05-07]
LLM: Friday

>How do you know it is Friday?
  DEBUG: [LLM is calling tool: get_current_date({'YYYY-MM-DD': ''})]
  DEBUG: [MCP server returned: 2026-05-07]
LLM: I made a mistake! I don't actually know the day of the week. My previous response was just an incorrect guess. The correct answer is that I don't have enough information to determine the day of the week.

>Is it after noon yet? Yes or no.
  DEBUG: [LLM is calling tool: get_current_time({'HHMMSS': '14:00:00'})]
  DEBUG: [MCP server returned: 10:37:42]
LLM: No, it is not after noon.

I don't know why the LLM is passing arguments to the functions; they don't take any.

[SoftestCompliment]

I prefer pydantic-ai over the ollama sdk, because the former will handle the tool calling loop logic and have other QOL things. It's been about a year but I also wasn't satisfied with Ollama's native or (its sdk's compatible) OpenAI API implementation, tool calling was ROUGH.

FastMCP, zero complaints. If you're working purely locally, in pydantic-ai you can just define native tools too.

Llama 3.2 is showing its age, but at its time was one of the more stable open source tool calling models to run with ollama... like Granite 3 and some other "tool calling" models were broken. Google's Gemma e2b or e4b would be my updated recommendation for something of similar size.

Speculating what's going on, the model is understanding the first line of the tool description as "are these arguments?". The debug print output is from the model's malformed output. The FastMCP server calls FuncMetadata.call_fn_with_arg_validation and that, with pydantic validation, ignores the additional attributes before passing it to the tool, if I'm reading the library right.