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rikarazome/prolog-reasoner

by Various

SWI-Prolog as a logic calculator for LLMs — MCP server and Python library

R

MCP

rikarazome/prolog-reasoner

Added 1 June 2026

#anthropic #claude #llm #logic-programming #mcp #mcp-server #prolog #python

Overview

A tool that provides an MCP server and Python library to use SWI-Prolog as a logic calculator for large language models. It enables LLMs to perform logical reasoning by invoking Prolog queries through a standardized interface.

Best for

Best for
Developers building LLM agents that need reliable, rule-based logical inference

Use cases

  • Running rule-based inference tasks within an LLM workflow
  • Validating logical constraints generated by an LLM
  • Combining natural language output with Prolog's deterministic deduction

Notes

A tool that provides an MCP server and Python library to use SWI-Prolog as a logic calculator for large language models. It enables LLMs to perform logical reasoning by invoking Prolog queries through a standardized interface.

8 stars on GitHub. Last updated 2026-05-01. Licensed MIT.

Use cases

  • Running rule-based inference tasks within an LLM workflow
  • Validating logical constraints generated by an LLM
  • Combining natural language output with Prolog’s deterministic deduction

Pros

  • Leverages Prolog’s mature reasoning engine for consistent logical output
  • Exposes logic calculus as an MCP server, compatible with LLM tool-use patterns
  • Simple Python library for embedding Prolog query execution

Cons

  • Small community and limited production usage (8 GitHub stars)
  • Requires familiarity with Prolog syntax and logic programming
  • Narrow focus only useful if LLM task needs formal deductive reasoning

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Leverages Prolog's mature reasoning engine for consistent logical output
  • Exposes logic calculus as an MCP server, compatible with LLM tool-use patterns
  • Simple Python library for embedding Prolog query execution

Cons

  • Small community and limited production usage (8 GitHub stars)
  • Requires familiarity with Prolog syntax and logic programming
  • Narrow focus only useful if LLM task needs formal deductive reasoning

Pairs with

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