Euclid-MCP server

Euclid-MCP

MCP server for logical reasoning — turns facts into formal proofs.

<!-- mcp-name: io.github.Euclid-BG/euclid-mcp -->

Euclid-MCP is a hybrid cognitive architecture: a lightweight LLM describes the world in facts, and a deterministic engine performs the actual deduction. The LLM never needs to reason — it only needs to describe.

With Euclid-MCP, an 8B model can solve reasoning tasks that stump even 400B+ cloud models — because the engine handles deduction deterministically. Every answer comes with a proof tree, so you can trace why a conclusion holds, not just what it is. Use it to enforce RBAC policies, audit cloud compliance, validate loan eligibility rules, or reason over any domain where answers must be explainable and verifiable.

Euclid-MCP is written in Python and uses Euclid-IR, a human-readable intermediate language designed for both AI agents and humans. It uses SWI-Prolog as its primary inference engine — and, where SWI-Prolog is not available (e.g. minimal containers), a pure-Python native engine that interprets Euclid-IR directly (see docs/NATIVEENGINE.md). It can be consumed in multiple ways: via MCP by AI agents (OpenCode, Claude, Cursor), via HTTP by tools and automation platforms (n8n, Zapier, Make), and via Python API for direct integration. Euclid-IR rules can also be used to augment RAG** pipelines with deterministic policy enforcement.

How it works

┌──────────────┐     ┌──────────────────┐     ┌──────────────┐     ┌─────────────────┐
│  LLM/Agent   │────▶│  Euclid-MCP      │────▶│  Translator  │────▶│  SWI-Prolog     │
│  (MCP Client)│◀────│  (MCPServer)     │◀────│  + Meta-IP   │◀────│  (persistent)   │
└──────────────┘     └──────────────────┘     └──────────────┘     └─────────────────┘

1. Receive facts, rules, and a query in a simple intermediate language
2. Translate into Prolog with a meta-interpreter for proof tree capture
3. Execute via a persistent SWI-Prolog engine process (JSON-lines protocol on stdin/stdout; the workspace is reloaded per call, no process spawn overhead)
4. Return solutions + proof trees as structured JSON

Additional tools (explain, diagnose, whatif, checkkb) extend this core flow with natural-language explanations, analysis, scenario testing, and validation.

LLMs describe. Euclid MCP proves.

Knowledge Base

For small knowledge bases, facts and rules can be provided with each request.

A knowledge base can be loaded at server startup and reused across calls, so agents only pass the session-specific facts for the current query. This minimizes token usage, improves performance, and allows small LLMs to reason over large rule sets without reconstructing the entire knowledge base for every request.

Intermediate Language

Even if currently Euclid-MCP uses a Prolog Engine, no Prolog syntax is required. Euclid-IR (Intermediate Representation) is a declarative intermediate representation for logical inference. Variables use $name, implication is IF, conjunction is AND.

Text format:

human(socrates)
mortal($x) IF human($x)

? mortal($who)

Shortened. The full README is on GitHub.