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OWL MCP Server

@ai4curation

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ai4curation

OWL MCP Server について

MCP server for OWL applications

#ai#mcp#semantic-web#ontology#web-ontology-language#model-context-protocol

OWL MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "owl-mcp": {
      "command": "uvx",
      "args": [
        "owl-mcp"
      ]
    }
  }
}

OWL MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

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

よくある質問

1

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.

2

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.

3

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.

4

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.

5

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.

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