OmniFocus MCP Server

The OmniFocus MCP Server acts as a bridge between AI assistants—such as Claude—and the OmniFocus task‑management ecosystem. By exposing a set of MCP resources, tools, and prompts, the server lets models read from, write to, and manipulate a user’s OmniFocus database entirely through natural‑language dialogue. This eliminates the need for manual copy‑paste or script‑writing, enabling developers to embed sophisticated task‑automation workflows directly into conversational agents.

At its core, the server offers a rich set of capabilities that mirror the full breadth of OmniFocus’s features: creating, updating, and deleting tasks or projects; tagging items with custom metadata; setting defer and due dates; and querying the database with complex filters. The tool, for example, allows a model to retrieve only the data it needs—such as flagged tasks due today or all items in a particular project—by specifying entity type, filters, sorting, and field selection. This targeted approach dramatically speeds up operations compared to a full database dump and keeps the response payload small.

Developers can leverage these tools in real‑world scenarios that require rapid, context‑aware task management. A typical use case is converting a meeting transcript or PDF syllabus into actionable OmniFocus items: the assistant parses the document, extracts action points, and creates tasks with appropriate tags, due dates, and notes. Another scenario involves bulk updates—adding an “energy level” tag to every task lacking it or re‑organizing projects based on priority—all triggered by a single conversational prompt. The server also supports perspective management, letting agents list available perspectives or fetch the current view’s items, which is invaluable for users who rely on custom task lenses.

Integration into AI workflows is straightforward: a client simply registers the MCP server in its configuration, and from that point forward any tool invocation is handled by the server over the MCP protocol. Because the server exposes both tools and prompts, developers can design custom conversational flows that combine natural language understanding with precise OmniFocus commands. The result is a seamless, end‑to‑end experience where the assistant not only understands what needs to be done but also executes it directly within the user’s task system.

Unique advantages of OmniFocus MCP include its native support for complex query parameters, the ability to perform batch operations in a single call, and tight integration with OmniFocus’s perspective feature—something that most generic task‑management APIs lack. These strengths make it an indispensable component for developers building productivity assistants that need to stay in sync with a user’s existing workflow while providing the flexibility and intelligence of an AI companion.