Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
Bartolomeo Bogliolo
Why It Matters
What makes this one worth your time
This work is relevant for AI researchers and engineers interested in improving the reliability of LLMs in logical reasoning tasks, especially in domains where accuracy and compliance are critical.
Euclid-MCP enhances logical reasoning by integrating Prolog with LLMs for more reliable inference.
Summary
The paper introduces Euclid-MCP, an open-source server that facilitates deterministic logical reasoning using SWI-Prolog, addressing the limitations of Large Language Models (LLMs) in multi-step logical reasoning. It proposes Euclid-IR, an intermediate representation for Horn-clause logic, and evaluates the system in an IT security and compliance context, showing improved performance over LLMs alone.
Key contributions
- Development of Euclid-MCP, a server for deterministic logical reasoning.
- Introduction of Euclid-IR, a human-readable intermediate representation for logic.
- Evaluation of the system in a realistic IT security and compliance use case.
Notable insights
- The use of an engine-agnostic intermediate representation (Euclid-IR) that is easy for LLMs to generate and compile.
- The translate-run-inspect-repair loop allows for detailed inspection and correction of logical inferences.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.21412v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially in safety-critical or compliance-sensitive domains. Recent neuro-symbolic approaches address this gap by coupling neural models with external symbolic engines, yet most integrations are bespoke and lack a standardized interface for tool-augmented agents. This paper presents Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. Euclid-MCP introduces Euclid-IR, an engine-agnostic intermediate representation for Horn-clause logic that is human-readable, easy for LLMs to generate, and straightforward to compile into Prolog or alternative backends. The server exposes a compact tool interface that supports a translate-run-inspect-repair loop, enabling LLM clients to delegate inference while retaining full access to proof traces and derivation logs. We evaluate Euclid-MCP on a realistic IT security and compliance use case. Results show that while LLMs alone are sufficient on small knowledge bases, they hallucinate systematically on larger problems, whereas Euclid-MCP delivers exact answers with lower latency and more compact outputs. We argue that semantic RAG is fundamentally unsuited for rule enforcement, and that Euclid-MCP can serve as a stable, shared reasoning substrate for both RAG-based assistants and agentic systems.