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yifanzhao2023/fan-enterprise-ontology-mcp

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An MCP server that exposes an enterprise ontology as an agent's semantic memory layer. Agents read the ontology schema, query named competency questions or raw Cypher, write validated instance data,...

Обзор

An MCP server that exposes an enterprise ontology as an agent's semantic memory layer. Agents read the ontology schema, query named competency questions or raw Cypher, write validated instance data, and consume offline-inferred derived facts — all as MCP tools. Storage and query layer, agent integration, offline inference, SHACL validation are in scope for v1. Ontology alignment and knowledge extraction are planned, not built. - MCP (stdio) - Python (FastMCP) + JVM inference CLI - Neo4j - Turtle / JSON-LD (RDF); tool results use a JSON-LD-aligned @context - Jena rules (OWL 2 RL + business rules), offline - pySHACL (SHACL Core at write time, full SHACL offline) - Apache License 2.0 An enterprise ontology here is a structured business semantic model — classes, relations, properties, constraints, and axioms — plus the instance facts that conform to it.

README

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fan-enterprise-ontology-mcp

Making enterprise ontology simpler. · 让企业本体更简单

An MCP server that exposes an enterprise ontology as an agent’s semantic memory layer.

Agents read the ontology schema, query named competency questions or raw Cypher, write validated instance data, and consume offline-inferred derived facts — all as MCP tools.

Language: English · 简体中文

Status: v1.0.2 — storage, query, agent integration, offline inference, SHACL validation, and the mapping-driven batch ingest with lineage audit are all built and tested. Knowledge extraction is planned, not built.

  • Protocol: MCP (stdio)
  • Language: Python (MCP SDK) + JVM inference CLI
  • Storage: Neo4j
  • Interchange: Turtle (RDF); tool results use a JSON-LD-aligned @context as a business contract layer, never as an RDF exchange format
  • Inference: Jena rules (OWL 2 RL + business rules), offline
  • Validation: pySHACL (SHACL Core at write time, full SHACL offline)
  • Ingest: mapping artifacts in Git + onto import (CLI, batch-atomic), with an append-only :IngestJob lineage row per attempt
  • License: Apache License 2.0

Index

1. Overview

An enterprise ontology here is a structured business semantic model — classes, relations, properties, constraints, and axioms — plus the instance facts that conform to it. Its value over a plain property graph or text index is the axioms and constraints: the model can assert that “the head of a department is necessarily the owner of that department’s work orders”, and derived facts follow from it.

This project does not build a modeling environment. It makes a human-authored ontology (in Git) usable by agents at runtime, and keeps the instance store consistent with that ontology through validation and offline inference.

TBox in Git, ABox in Neo4j. The authoritative ontology is versioned Turtle/JSON-LD. Neo4j holds instances plus two derived artifact classes. The schema an agent reads is a flat projection generated from the TBox by CI.

2. Target Users & Scenarios

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Установка

This server does not publish a one-line install command.

Open the repository installation guide Новичок в MCP? Начните с руководства на mcpskill.org →

Конфигурация

{ "mcpServers": { "feomcp": { "command": "/abs/path/to/.venv/bin/feomcp", "args": [], "env": { "FEOMCP_ONTOLOGY_DIR": "/abs/path/to/ontology", "FEOMCP_NEO4J_URI": "bolt://localhost:7687", "FEOMCP_NEO4J_USER": "reader", "FEOMCP_NEO4J_PASSWORD": "…", "FEOMCP_NEO4J_WRITE_USER": "writer", "FEOMCP_NEO4J_WRITE_PASSWORD": "…" } } } }