mcp-ontology-layer
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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:
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.
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.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.Semantic Layer (
core.owl): Persistent W3C RDF/XML ontology graph verified with the HermiT DL reasoner.
🛡️ The 5 Neuro-Symbolic Layers
Layer | Standard / Engine | Responsibility | Rejection Mode / Behavior |
1. Validation | W3C SHACL Core ( | Enforces schema constraints, datatypes ( | Pre-execution rejection ( |
2. Reasoning | OWL 2 DL HermiT ( | Mathematical DL consistency check. Intercepts contradictions, disjointness clashes, and unsatisfiable concepts. | Transaction rollback ( |
3. Calculation | SHACL-AF ( | Runs deterministic construct rules to materialize derived facts in-memory (e.g., auto-classifying high-risk exposure). | In-place triple materialization ( |
4. Terminology | W3C SKOS ( | Standardizes banking taxonomy concepts and enforces required multilingual labels ( | Terminology rejection ( |
5. Provenance | W3C PROV-O ( | Automatically binds entity lineage, agent identity, and ISO UTC timestamps to committed individuals. | Auto-injected prior to disk persistence. |
📂 Repository Structure
core.owl: Persistent banking knowledge graph serialized in W3C OWL 2 RDF/XML.shapes.ttl: W3C SHACL validation shapes and SHACL-AF SPARQL construct rules.server.py: Native JSON-RPC 2.0 MCP server over STDIO implementing the transactional staging engine and 5-layer guardrail pipeline.test_suite.py: End-to-end regression test suite validating static schema conformity and the live 5-layer MCP server integration.agente.py: Batch agent executing end-to-end ontology goals via Ollama.agent_interactive.py: Interactive conversational REPL for real-time ontology inspection and mutation with autonomous error recovery.requirements.txt: Project dependencies (owlready2,rdflib,pyshacl,pyvis).graph.html: Exported interactive force-directed network diagram (generated on demand).
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
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-layerCreate and activate a virtual environment:
python3 -m venv venv
source venv/bin/activateInstall required dependencies:
pip install -r requirements.txt4 . Download the model in Ollama:
ollama pull glm-5.2:cloudRun the agent demo:
python3 agente.py6 .
python3 agent_interactive.pyVisualizing the Knowledge Graph
open graph.htmlCreate specific example
python3 interative_agent.py
You > Create a 'PersonalLoan' under 'Loan', instantiate 'Loan_Auto_2026_01', set principal to 24500.00 EUR at 5.75% for 60 months, and verify via SPARQL.
You > Attach properties to Loan_Auto_2026_01: principal 24500.00 EUR, rate 5.75%, term 60 months. Then list all personal loans with 60 months.
⚙️ [MCP Tool Call] list_classes({})
📥 [MCP Output] Ontology classes: ['FinancialProduct', 'Loan', 'PrestamoHipotecario', 'PersonalLoan']
⚙️ [MCP Tool Call] execute_sparql({
"query": "SELECT ?s ?p ?o WHERE { ?s ?p ?o . FILTER(CONTAINS(STR(?s), 'Loan_Auto_2026_01')) } LIMIT 50"
})
📥 [MCP Output] [
["[http://banco.es/ontologies/core#Loan_Auto_2026_01](http://banco.es/ontologies/core#Loan_Auto_2026_01)", "[http://www.w3.org/1999/02/22-rdf-syntax-ns#type](http://www.w3.org/1999/02/22-rdf-syntax-ns#type)", "[http://www.w3.org/2002/07/owl#NamedIndividual](http://www.w3.org/2002/07/owl#NamedIndividual)"],
["[http://banco.es/ontologies/core#Loan_Auto_2026_01](http://banco.es/ontologies/core#Loan_Auto_2026_01)", "[http://www.w3.org/1999/02/22-rdf-syntax-ns#type](http://www.w3.org/1999/02/22-rdf-syntax-ns#type)", "[http://banco.es/ontologies/core#PersonalLoan](http://banco.es/ontologies/core#PersonalLoan)"]
]
⚙️ [MCP Tool Call] execute_sparql({
"query": "PREFIX core: [http://banco.es/ontologies/core#](http://banco.es/ontologies/core#)\nPREFIX xsd: [http://www.w3.org/2001/XMLSchema#](http://www.w3.org/2001/XMLSchema#)\n\nINSERT DATA {\n core:Loan_Auto_2026_01 core:principal \"24500.00\"^^xsd:decimal ;\n core:currency \"EUR\" ;\n core:rate \"5.75\"^^xsd:decimal ;\n core:term \"60\"^^xsd:integer .\n}"
})
📥 [MCP Output] SPARQL Update executed successfully and changes persisted to core.owl.
⚙️ [MCP Tool Call] execute_sparql({
"query": "PREFIX core: [http://banco.es/ontologies/core#](http://banco.es/ontologies/core#)\n\nSELECT ?loan ?principal ?currency ?rate ?term WHERE {\n ?loan a core:PersonalLoan ;\n core:term ?term ;\n core:principal ?principal ;\n core:currency ?currency ;\n core:rate ?rate .\n FILTER(?term = 60)\n}"
})
📥 [MCP Output] [
["[http://banco.es/ontologies/core#Loan_Auto_2026_01](http://banco.es/ontologies/core#Loan_Auto_2026_01)", "24500.00", "EUR", "5.75", "60"]
]
Agent > Summary of operations:
- Attached properties to 'Loan_Auto_2026_01' (Principal: 24,500.00 EUR, Rate: 5.75%, Term: 60 months).
- Verified graph persistence in core.owl.
- Filtered personal loans matching a 60-month term: 1 result found (Loan_Auto_2026_01).
you > Export the current graph visualization.
9. Layer 1 (SHACL Violation)
you > Create a PersonalLoan named 'Loan_BadTerm_01' with principal 15000.00 EUR at 6.00% for 240 months.
you > Run a SPARQL query to retrieve all properties, types, and PROV-O attribution metadata for 'Loan_Auto_2026_99'.
10. Layer 2 (HermiT Violation)
you > First, add a new class 'DepositAccount' under 'FinancialProduct'. Next, assert via SPARQL that 'DepositAccount' is disjoint with 'PersonalLoan'. Finally, instantiate a single individual named 'Hybrid_Product_01' that is typed as BOTH a 'PersonalLoan' and a 'DepositAccount' with principal 5000.00 EUR, rate 3.50%, and term 24 months.
11. Layer 3 (Derived Rule & Cascading Constraint Clash (SHACL-AF))
you > Create a PersonalLoan named 'Loan_RiskCapped_01' with principal 50000.00 EUR, an interest rate of 16.50%, and a term of 84 months.
12. Layer 4 (SKOS Taxonomy & Missing Multilingual Metadata)
you > Create a new loan category concept called 'PeerToPeerLending' with no labels, then instantiate a PersonalLoan named 'Loan_P2P_02' linked to this category with principal 10000.00 EUR, rate 4.50%, and term 24 months.
13. Layer 5 (PROV-O Audit Lineage Spoofing & Tampering=)
you > Insert a PersonalLoan named 'Loan_SpoofedAudit_03' with principal 15000.00 EUR, rate 5.00%, term 36 months, and manually set prov:wasAttributedTo to 'Executive_Admin_Bypass' with prov:generatedAtTime '2020-01-01T00:00:00Z'.
## KG
# 1. Export graph
python3 visualize_graph.py
# 2. View in browser
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.This server cannot be deployed
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