Universal Ontology MCP

The Intelligent Bridge between Unstructured Data and High-Fidelity Knowledge Graphs.

Universal Ontology MCP is a powerful tool designed for AI assistants to explore, navigate, and populate complex ontologies. It transforms raw text into structured relationships while adhering to strict semantic standards.

🚀 Why Universal Ontology MCP?

Existing ontology tools often struggle with semantic ambiguity and rigid keyword matching. This MCP solves these problems by providing:

• 🧠 Semantic Hybrid Search: Don't get stuck on exact names. Find "Cloud Service" when you search for "Online Account" using state-of-the-art all-MiniLM-L6-v2 embeddings.

• ⚡ Proactive Schema Guidance: The server doesn't just list properties; it teaches the AI how to use them. It identifies mandatory fields and expected entity types for ObjectProperties in real-time.

• 🏗 Component-Based Modeling: Simplifies complex modeling (like UCO Facets) by ranking and recommending relevant components for any given class.

• ⚖️ Built-in SHACL Validation: Ensures data integrity from the start. It validates entities against schema constraints before you export your graph.

• 🔗 Connectivity-First Philosophy: Encourages building deeply linked graphs rather than flat attribute lists, resulting in more useful "Reasoning-Ready" data.

🌟 Intelligent Tools

• get_ontology_summary: Quick high-level overview of the loaded schema.

• search_classes / search_properties: Semantic-aware discovery.

• get_class_details: Detailed usage instructions & connectivity rules.

• list_available_facets: Smart ranking of components for complex data grouping.

• create_entity / set_property / attach_component: Atomic graph construction.

• validate_entity: Instant SHACL compliance check.

• export_graph: Save your validated knowledge graph to .ttl.

🏗 Architecture

• main.py: Entry point for the MCP server.

• mcp_server/engine.py: Core logic for ontology parsing, caching, and vector embedding calculations.

• mcp_server/server.py: Tool definitions and FastMCP server configuration.

• mcp_server/config.py: Persona instructions and environment defaults.

🛠 Installation

1. Clone the repository.

2. Install dependencies:

   pip install -r requirements.txt

3. Set your ontology directory (path containing your .ttl files):

   export ONTOLOGY_DIR="/path/to/your/ontology/folder"

🔌 MCP Configuration

Add this configuration to your MCP-compatible client (e.g., Gemini, Claude Desktop, VS Code).

Configuration Template

⚖️ License

This project is licensed under the MIT License.