OWL-MCP
About
OWL-MCP is a Model-Context-Protocol (MCP) server that enables AI assistants to interact with Web Ontology Language (OWL) ontologies through standardized function calls. It is designed for ontology engineers, researchers, and developers working with OWL ontologies—especially…
Details
- Author
- scimcp
- GitHub stars
- 17
- Downloads
- 332
- Categories
- Other, AI
Jump to
- MCP Server Integration: connects AI assistants to OWL ontologies via the standardized protocol
- Thread‑safe operations: supports concurrent access in multi‑user environments
- File synchronization: automatically detects and syncs changes to the ontology file on disk
- Event‑based notifications: register observers to be notified of ontology changes
- Simple string‑based API: work with OWL axioms as strings in functional syntax
- Label support: includes human‑readable labels for opaque IDs (common in OBO ontologies)
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
OWL-MCPCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install any MCP‑enabled AI host (recommended: Goose Desktop or CLI) and add the OWL‑MCP extension using the command uvx owl-mcp. Once configured, ask the AI to create an ontology or add axioms; the server handles reading/writing the OWL file on disk and keeps an in‑memory model synchronized.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"owl-mcp": {
"owl-mcp": {
"command": "uvx",
"args": [
"owl-mcp"
]
}
}
}
}McpServers
{
"owl-mcp": {
"command": "uvx",
"args": [
"owl-mcp"
]
}
}OWL-MCP
OWL-MCP is a Model-Context-Protocol (MCP) server for working with Web Ontology Language (OWL) ontologies.
Quick Start
This walks you through using owl-mcp with Goose, but any MCP-enabled AI host will work.
Install Goose
You can use either the Desktop or CLI version of Goose from here:
Follow the instructions for setting up an LLM provider (Anthropic recommended)
Install OWL-MCP extension
You can either install directly from this link:
Or to do this manually, in the Extension section of Goose, add a new entry for owlmcp:
uvx owl-mcp
This video shows how to do this manually:
Try it out
You can ask to create an ontology, and add axioms to an ontology:
How this works
The MCP server provides function calls for finding, adding, or removing OWL axioms, using OWL functional syntax. Each function call is accompanied by the file path of the OWL file on your disk. Any format supported by py-horned-owl is accepted (we following OBO guidelines and recommend functional syntax for source).
The server takes care of keeping an instance of the ontology in memory and syncing it with disk. Any CRUD operation simultaneously updates the in-memory model and syncs this with disk. If you have Protege running, Protege will also
sync with local disk, and show updates.
The server is well adapted for working with OBO-style ontologies - when OWL strings are sent back to the client, labels for opaque IDs are included after #s comments, as is common for obo-format.
Key Features
- MCP Server Integration: Connect AI assistants directly to OWL ontologies using the standardized Model-Context-Protocol
- Thread-safe operations: All ontology operations are thread-safe, making it suitable for multi-user environments
- File synchronization: Changes to the ontology file on disk are automatically detected and synchronized
- Event-based notifications: Register observers to be notified of changes to the ontology
- Simple string-based API: Work with OWL axioms as strings in functional syntax without dealing with complex object models
- Configuration system: Store and manage settings for frequently-used ontologies
- Label support: Access human-readable labels for entities with configurable annotation properties
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
