Local MCP Semantic Agent with OWL, HermiT & Ollama

A fully local execution architecture on macOS for deterministic inspection, mutation, Description Logic (DL) reasoning, and SPARQL querying of banking ontologies (OWL/RDF) using LLM agents and the Model Context Protocol (MCP) over STDIO.

Core Capabilities

• HermiT DL Reasoning: Integrated Description Logic reasoner (sync_reasoner_hermit) to verify ontology consistency, compute inferred class hierarchies, and detect unsatisfiable classes.

• SPARQL Query Engine: Direct W3C SPARQL query execution over the RDF knowledge graph via RDFLib, allowing complex semantic queries, filtering, and aggregations.

• Taxonomy & Instance Mutation (TBox/ABox): Dynamic insertion of OWL classes, subclass hierarchies, and named individual assertions.

• Interactive Force-Directed Visualizations: Automatic export of the knowledge graph into interactive PyVis HTML networks.

• Dual Execution Modes: Automated batch pipeline (agente.py) and persistent conversational REPL (agent_interactive.py).

Related MCP server: OWL-MCP

Architecture

The system decouples model reasoning from graph execution across four core layers:

1. Agent Orchestrator:

   • agente.py: Automated pipeline executing batch agent goals end-to-end.

   • agent_interactive.py: Multi-turn conversational REPL maintaining state and executing dynamic multi-step semantic workflows.

2. LLM Engine (Ollama): Executes local models (e.g., glm-5.2:cloud, qwen2.5-coder) to perform intent decomposition and parameter formatting without direct file access.

3. MCP Server (server.py): JSON-RPC 2.0 interface communicating over STDIO, translating model tool calls into deterministic semantic graph operations via Owlready2 and RDFLib.

4. Semantic Layer (core.owl): Persistent W3C RDF/XML ontology graph verified with the HermiT DL reasoner.

Repository Structure

• core.owl: Base banking ontology file in RDF/XML format (W3C OWL Standard).

• server.py: Native JSON-RPC 2.0 MCP server providing TBox/ABox mutation, SPARQL execution, HermiT reasoning, and PyVis graph exports.

• agente.py: Automated batch execution agent connecting Ollama to the MCP server.

• agent_interactive.py: Interactive conversational REPL agent for real-time ontology management.

• requirements.txt: Python dependencies (owlready2, rdflib, pyvis).

• graph.html: Exported interactive HTML graph visualization (generated on demand).

• README.md: Project architecture, tool documentation, and setup instructions.

Prerequisites

• OS: macOS (Apple Silicon or Intel)

• Python: 3.10 or higher

• Ollama: Installed and running locally

• Java: OpenJDK / JRE (required by Owlready2 for the HermiT reasoner)

Installation & Environment Setup

1. Clone the repository and navigate to the project directory:

git clone [https://github.com/jairorodriguezarias/mcp-ontology-layer.git](https://github.com/jairorodriguezarias/mcp-ontology-layer.git)
cd mcp-ontology-layer

2. Create and activate a virtual environment:

python3 -m venv venv
source venv/bin/activate

3. Install required dependencies:

pip install -r requirements.txt

4 . Download the model in Ollama:

ollama pull glm-5.2:cloud

5. Run the agent demo:

python3 agente.py

6 .

python3 agent_interactive.py

7. Visualizing the Knowledge Graph

open graph.html

Available MCP Tools

• list_classes

• Parameters: None

• Description: Loads core.owl and returns all registered ontology classes.

• add_subclass

• Parameters: new_class (string, required), parent_class (string, required)

• Description: Inserts a new class under an existing parent class and saves the updated RDF/XML graph to disk.

• create_individual

• Parameters: class_name (string, required), individual_id (string, required)

• Description: Instantiates a concrete ABox individual belonging to a specific class.

• check_consistency

• Parameters: None

• Description: Runs the HermiT Description Logic (DL) reasoner to verify logical consistency and detect unsatisfiable classes.

• execute_sparql

• Parameters: query (string, required)

• Description: Executes a standard W3C SPARQL query against the RDF knowledge graph.

• export_graph

• Parameters: output_html (string, optional; default: graph.html)

• Description: Generates an interactive force-directed HTML graph visualization of the ontology using PyVis.