What does OWL-MCP do?
It provides MCP function calls for finding, adding, or removing OWL axioms in a local ontology file, keeping an in‑memory model synced with the disk.
OWL MCP Server
@ai4curation
Basic information
About OWL MCP Server
MCP server for OWL applications
OWL MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"owl-mcp": {
"command": "uvx",
"args": [
"owl-mcp"
]
}
}
}OWL MCP Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
Overview
What is OWL-MCP?
OWL‑MCP is a Model‑Context‑Protocol (MCP) server for working with Web Ontology Language (OWL) ontologies. It lets AI assistants—via any MCP‑enabled host—add, find, or remove OWL axioms stored on disk, keeping an in‑memory model in sync with the file.
How to use OWL-MCP?
Install an MCP‑enabled host such as Goose (desktop or CLI), then add OWL‑MCP as an extension using uvx owl-mcp. After configuration, you can ask the AI to create an ontology or add axioms; each operation references a local OWL file path. Any format supported by py‑horned‑owl is accepted, and OWL functional syntax is used for the API calls.
Key features of OWL-MCP
- MCP integration for AI‑assisted ontology editing
- Thread‑safe operations for multi‑user environments
- Automatic file synchronization with disk
- Event‑based notifications via registered observers
- Simple string‑based API using OWL functional syntax
- Configuration system for frequently‑used ontologies
- Human‑readable labels for entities (OBO‑style support)
Use cases of OWL-MCP
- Create a new OWL ontology entirely through natural‑language prompts
- Add or remove axioms interactively while working in tools like Protégé
- Maintain OBO‑style ontologies with label comments after opaque IDs
- Automate ontology updates from a multi‑user AI workflow
- Synchronise changes between an AI assistant and a shared OWL file on disk
Frequently asked questions
What dependencies or runtime are required?
OWL‑MCP requires `uvx` to run and any MCP‑enabled AI host (e.g., Goose). It uses the py‑horned‑owl library under the hood to parse and write OWL files in any supported format.
Where is the ontology data stored?
The ontology data lives in a local OWL file on your disk. All CRUD operations read from and write to that file, and the server syncs the in‑memory model automatically.
Can I use OWL-MCP with Protégé?
Yes. Because the server synchronises changes to disk, Protégé—if pointed at the same file—will automatically see updates.
What syntax does OWL-MCP use for OWL axioms?
The API works with OWL axioms as strings written in OWL functional syntax, which is also the recommended source format following OBO guidelines.
More Other MCP servers
Activepieces
activepiecesAI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
23.2K
Lemon.io
lemon-ioRequest dedicated senior developers to work on your project, write job descriptions, and prep technical interviews — all without leaving your Claude chat. Just describe what you need, and Lemon.io MCP does the rest. 1–3
Maestro
mobile-dev-incPainless E2E Automation for Mobile and Web
14.8K
Volley.sh
VolleyA good sequence diagram can solve almost any communication problem in engineering. Volley lets your agent write them as plain text, with the real request and response payloads on every arrow. It checks its own syntax wi
MaxKB
1Panel-dev🔥 MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。
22.0K
Comments