Omeka S MCP Sample

Overview

The Omeka S MCP Sample is a ready‑to‑run MCP server that bridges the powerful, web‑based digital collections platform Omeka S with Claude Desktop. By exposing a concise set of MCP tools, the server lets an AI assistant query and manipulate Omeka S collections directly from within a conversational interface, eliminating the need for manual API calls or custom client code.

This MCP solves a common pain point for developers and archivists: integrating rich, structured metadata and media assets into AI‑driven workflows. Without this server, a user would have to write HTTP requests, handle authentication tokens, and parse JSON responses each time they wanted to list items or upload new media. The server abstracts these details behind simple, declarative tool calls such as or , enabling a natural language prompt to perform complex collection management tasks.

Key capabilities are presented in plain language:

• List items – Retrieve a paginated list of all items, giving the AI assistant an overview of existing content.
• Get item – Fetch detailed metadata for a specific item by its identifier, allowing the assistant to display or edit information.
• Create item – Create a new collection record with title and optional description, supporting rapid content ingestion.
• Upload media – Attach files or remote URLs to an item, streamlining the addition of images, audio, or other media.

These tools are built on top of aiohttp for efficient asynchronous requests and follow MCP 1.6+ conventions, ensuring compatibility with the latest Claude Desktop client. The server’s configuration is straightforward: a single file holds the Omeka S API URL and credentials, while the client registers the server with a command line entry. Once registered, any Claude prompt can invoke these tools, and the assistant can seamlessly pull data from or push content to Omeka S.

Real‑world use cases abound: a museum curator can ask the assistant, “Show me all items in the Ancient Egypt collection,” or “Add a new photograph of the Rosetta Stone.” A research team can automate data ingestion by feeding lists of metadata into and linking related media with . Because the server exposes a clean, well‑documented API surface, developers can extend or customize it—adding new tools for batch updates or advanced search—without touching the core MCP logic.

In summary, the Omeka S MCP Sample empowers developers and content managers to harness AI for collection curation, data entry, and media management. By turning routine API interactions into conversational commands, it accelerates workflows, reduces boilerplate code, and opens the door to creative integrations that were previously cumbersome or impossible.