Owl MCP

🦉 OWL MCP Server

Let AI read and write your OWL / TTL / RDF ontology files directly

English | 简体中文

Quick Start · Tools · Examples

---

Introduction

OWL MCP Server is an ontology manipulation server built on the Model Context Protocol (MCP).

It lets AI assistants such as Claude, Cursor, and WorkBuddy query, modify, and reason over your OWL/TTL/RDF ontology files as easily as ordinary files — no hand-written SPARQL, no OWL syntax details; complex knowledge-graph operations done through natural-language conversation.

You: "Find all individuals of class XX, sorted by amount in descending order"
AI:  [calls sparql_query] → returns structured results

Use Cases

• 🏦 Finance: CRUD on bank-statement and invoice ontologies
• 🏥 Healthcare: exploration and reasoning over clinical knowledge graphs
• 🏗️ Engineering: management and validation of equipment and process ontologies
• 🎓 Research: rapid prototyping and testing of OWL ontologies

---

Quick Start

Step 1: Install dependencies

pip install mcp owlready2 rdflib

Or via requirements.txt:

pip install -r requirements.txt

⚠️ Reasoners need Java: run_reasoner (Pellet/HermiT) requires a local JDK 8+ installation.

Step 2: Configure your MCP client

WorkBuddy

Add to ~/.workbuddy/mcp.json:

{
  "mcpServers": {
    "owl": {
      "command": "python",
      "args": ["/absolute/path/server.py"]
    }
  }
}

💡 Recommended: use the absolute path of a virtualenv Python to avoid dependency conflicts:

"command": "C:/Users/yourname/.workbuddy/binaries/python/envs/default/Scripts/python.exe"

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "owl-mcp": {
      "command": "python",
      "args": ["/absolute/path/server.py"]
    }
  }
}

Cursor

Add the same configuration to .cursor/mcp.json, restart Cursor, and the server is available in Agent mode.

Step 3: Enable it and start talking

In the WorkBuddy connector management page, find owl, click Trust to enable it, then just chat in natural language:

"Load the ontology file C:/work/xxxx.ttl"
"List all classes, only the ones starting with xxx:"
"Describe the full structure of the BankStatement class"
"Run the HermiT reasoner to check ontology consistency"
"Save the new classes back to the original file"

---

Tools

📖 Query tools

✏️ Modification tools

🧠 Reasoning tools

Note: add_* operations are not saved automatically — call save_ontology explicitly.

---

Examples

Exploring the ontology structure

You: Load xxx.ttl and give me an overview
AI:  This ontology has 47 classes, 213 individuals, 18 object properties...

You: Describe the BankStatement class
AI:  BankStatement is a subclass of FinancialDocument, with the following data
     properties: amount, currency, date...

SPARQL queries

-- Find all BankStatement individuals and their amounts (descending)
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX : <http://test.com/t#>

SELECT ?ind ?amount WHERE {
  ?ind rdf:type :BankStatement .
  ?ind :amount ?amount .
}
ORDER BY DESC(?amount)

Incremental modification

You: Add a new class AA whose parent is BB, with label "CC"
AI:  [calls add_class] → class AA added

You: Save the change in turtle format
AI:  [calls save_ontology] → saved to xxx.ttl

Consistency checking

You: Run the HermiT reasoner and check the ontology for logical contradictions
AI:  [calls run_reasoner] → the ontology is consistent; 3 new implicit class
     memberships were inferred...

---

Tech Stack

---

Notes

• Reasoners need Java: Pellet / HermiT depend on JDK 8+; make sure java is on your system PATH
• Large ontologies: beyond ~100k triples, use the limit parameter with the list_* tools
• Changes are not auto-saved: all add_* operations only update the in-memory model; call save_ontology explicitly
• SPARQL syntax: standard SPARQL 1.1, executed by the rdflib engine

---

Compatible MCP Clients

• ✅ WorkBuddy
• ✅ Trae/Traw-work
• ✅ Claude
• ✅ Cursor (Agent mode)
• ✅ Any MCP-compliant AI client

---

License

MIT

---

☕ Buy Me a Coffee

OWL MCP Server is an open-source project I develop and maintain in my spare time, and it will stay free forever.

If it has saved you even half an hour of hand-writing SPARQL, consider buying me a coffee (¥9.9 is enough ☕) — every bit of support goes straight into new features and bug fixes.

When donating, feel free to mention the feature you want most — I'll prioritize it 😉

Prefer not to donate? Giving the project a ⭐ Star, opening an Issue, or sharing it with someone who needs it is equally great support!

---