Prolog Reasoner MCP server

<!-- mcp-name: io.github.rikarazome/prolog-reasoner -->

prolog-reasoner

SWI-Prolog as a "logic calculator" for LLMs — available as an MCP server and a Python library. Eliminate the black box from LLM logical reasoning.

LLMs excel at natural language but struggle with formal logic. Prolog excels at logical reasoning but can't process natural language. prolog-reasoner bridges this gap by exposing SWI-Prolog execution to LLMs.

Does it help?

On the built-in 30-problem logic benchmark:

The gap concentrates in constraint satisfaction and multi-step reasoning — the combinatorial territory LLMs are weak on and Prolog is strong on. Full breakdown below.

Why it works

LLMs pattern-match; Prolog actually searches and solves. When the LLM writes its problem down as Prolog, two things happen at once:

• Prolog handles the combinatorial work LLMs are weak on — constraint satisfaction, multi-step inference, exhaustive search.
• The reasoning exists as code you can read, re-run, and debug. When it goes wrong, you see the exact Prolog that failed and why.

Two ways to use it

• MCP server — Claude (or any MCP client) calls it as a logic solver during conversation. Rule bases let the LLM save stable domain rules once and reference them by name per call.
• Python library — full NL→Prolog pipeline with self-correction. Requires OpenAI or Anthropic.

Features

• MCP tools: executeprolog for arbitrary SWI-Prolog execution, plus listrulebases / getrulebase / saverulebase / deleterule_base for reusable named rule bases (v14)
• Rule bases: save stable Prolog rules once (e.g. chess move rules, legal axioms) and reference them by name from execute_prolog so the LLM only writes the situation-specific facts per call
• Transparent intermediate representation: the Prolog code is the audit trail — inspect, modify, or verify before execution
• CLP(FD) support: constraint logic programming for scheduling and optimization
• Negation-as-failure, recursion, all standard SWI-Prolog features
• Library mode: NL→Prolog translation with self-correction loop (OpenAI / Anthropic)

Requirements

• Python ≥ 3.10
• SWI-Prolog installed and on PATH (≥ 9.0)
• API key for OpenAI or Anthropic — only for library mode, not for the MCP server

Installation

# MCP server only (no LLM dependencies)
pip install prolog-reasoner

# Library with OpenAI
pip install prolog-reasoner[openai]

# Library with Anthropic
pip install prolog-reasoner[anthropic]

# Both providers
pip install prolog-reasoner[all]

MCP Server Setup

The MCP server exposes five tools — executeprolog runs Prolog code written by the connected LLM, and four rule-base tools manage named, reusable Prolog modules. It does not** call any external LLM API, so no API key is required.

Claude Desktop / Claude Code

{
  "mcpServers": {
    "prolog-reasoner": {
      "command": "uvx",
      "args": ["prolog-reasoner"]
    }
  }
}

Or, if prolog-reasoner is installed directly:

{
  "mcpServers": {
    "prolog-reasoner": {
      "command": "prolog-reasoner"
    }
  }
}

Docker (SWI-Prolog bundled)

Use Docker if you don't want to install SWI-Prolog locally:

Shortened. The full README is on GitHub.