Python Prolog MCP server, using SWI Prolog

Nice to know
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1

Hi,

I put together a Python MCP server that connects to a local SWI-Prolog instance, enabling standardized tool integration for agentic workflows.

This allows MCP clients (e.g., LLMs) to:

  • Autodetect Prolog tools (start server, add clauses, run queries)
  • Facilitate bidirectional communication between LLMs and Prolog

Repo: github.com/wendelinism/prolog-mcp-server

Features

  • MCP-compliant: Tools are auto-discoverable by MCP clients
  • Flexible I/O: Runs as an HTTP server or via stdio
  • Dockerized Prolog: Uses swi-prolog in Docker for easy setup

Getting Started

  1. Clone the repo and build locally (see README)
  2. Install SWI-Prolog via Docker
  3. Run either in HTTP mode or stdio for direct piping

Note: Currently tested on Linux only. No PyPI-package (yet)

I hope this to be useful for other people as well, while experimenting on the (agentic) integration of LLMs and Prolog.
Any feedback welcome.

2

Interesting. I have very little knowledge about MCP. Could we use this to improve ChatGPT - SWI Prolog Assistant?

I’m happy to host such a server on swi-prolog.org. In particular if there is someone willing to maintain and extend it :slight_smile:

3

Hi Jan, let me check out the ChatGPT-SWI Prolog Assistant when I am back at my laptop next week. My guess is that, if you already did set up a custom interface between LLM and Prolog server, MCP will not add functionality directly.

Main usecase for MCP I see:
Allow any LLM agent/client (that knows MCP) to dynamically access the Prolog MCP server (without having to set up a custom interface) and to “decide” whether the write Prolog code and run it.
I think it would be great to have a publically available Prolog MCP Server, I had not thought of that.

Overall the open question is, I think, whether Prolog can actually add to the reasoning capabilities of an LLM agent. Obviously LLMs themselves are pretty poor in logical reasoning.
That being said, may limited tests (and I believe those of others) on using LLM+Prolog were pretty sobering. Of course the prolog results can only be as good as the Prolog code. In my tests the LLM (openai:gpt-4o) did often very poor in translating a reasoning question into consistent prolog code (clauses + query).

4

I did not. The ChatGPT-SWI Prolog Assistant is a simple shared gpt that only has some instructions on its role and to focus it on publicly available information on SWI-Prolog as far as possible. I am fairly impressed on how well it answers questions. It isn’t very good at programming, but it seems pretty good and finding build-ins and libraries you need to glue together to get something done. After which it writes sloppy code connecting the ends … Well, my experience with C is not much better.

If you can help improving on the situation, please do. I’m glad to hand over this stuff to anyone more knowledgeable :slight_smile:

5

I agree it would be nice to allow the ChatGPT-SWI Prolog Assistant to directly run Prolog Code (via an MCP server).

Right now I see at least two things preventing us from doing that:

  1. OpenAI did not roll out MCP support for the ChatGPT interface yet (they started making it available for deep research, though)
  2. Up to now I implemented the prolog-mcp-server for single-users only, so each user is meant to start the server, no isolation from other users is in place.

I will work on #2 and will make use of pengines. I think this way we should be able to run one instance of the server and make it accessible for multiple chatgpt users (once openai support that).

6

Good plan. We can use the SWISH server as pengine server for a public version.