OmniFocus MCP

OmniFocus MCP

MCP server that gives AI assistants full control over OmniFocus on macOS, including tasks, projects, tags, folders, perspectives, forecast, notifications, and review workflows.

Category
访问服务器

README

OmniFocus MCP

Platform: macOS Protocol: MCP Language: Rust/Python/TypeScript License: MIT

MCP server that gives AI assistants full control over OmniFocus on macOS.

45 tools, 3 resources, and 4 prompts covering tasks, projects, tags, folders, perspectives, forecast, notifications, and review workflows — all through the Model Context Protocol.

This project is not affiliated with, endorsed by, or associated with The Omni Group or OmniFocus. OmniFocus is a trademark of The Omni Group. This is an independent, non-commercial open-source project.

Quick Start

Install via Homebrew (if you don't have Homebrew, see the Homebrew installation guide):

brew tap vitalyrodnenko/omnifocus-mcp
brew install omnifocus-mcp

Then add to your MCP client config (Claude Desktop, Cursor, etc.):

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

That's it. The AI assistant now has full OmniFocus access.

What It Can Do

Tasks (23 tools)

Full lifecycle management for OmniFocus tasks:

  • CRUD — create, get, update, delete individual tasks
  • Batch operations — create, move, or delete multiple tasks in a single call
  • Subtasks — create and list subtasks under any parent task
  • Completion — mark complete, mark incomplete (supports repeating tasks)
  • Search — full-text search across task names and notes with all filters applied
  • Move and reparent — relocate tasks between projects, reparent tasks under other tasks, or move subtasks back to inbox/project without delete/recreate
  • Duplicate — clone a task with all properties and optional subtasks
  • Notifications — list, add, and remove notifications (absolute date or relative offset)
  • Repetition — set or clear repetition rules with schedule type (regularly/after completion)
  • Notes — append text to task notes without overwriting
  • Safety model — destructive delete confirmations stay separate from non-destructive move/update workflows
  • Aggregate counts — fast "how many" queries without listing individual tasks

Advanced Filtering

list_tasks and search_tasks support powerful filter combinations:

Filter Description
project Scope to a single project by name
tag / tags Filter by one tag or multiple tags
tagFilterMode "any" (default) or "all" for multi-tag filtering
flagged Flagged tasks only
status "available", "remaining", "completed", "dropped", "all"
dueBefore / dueAfter Due date range (ISO 8601)
deferBefore / deferAfter Defer date range (ISO 8601)
completedBefore / completedAfter Completion date range (ISO 8601)
addedBefore / addedAfter Creation date range (ISO 8601)
changedBefore / changedAfter Last-modified date range (ISO 8601, maps to OmniFocus modified)
plannedBefore / plannedAfter Planned date range (ISO 8601)
maxEstimatedMinutes Tasks with estimated duration up to N minutes

Sorting

All list/search tools support sortBy and sortOrder:

  • Sort by: name, dueDate, deferDate, completionDate, estimatedMinutes, project, flagged, addedDate, changedDate, plannedDate
  • Aliases: added -> addedDate, modified -> changedDate, planned -> plannedDate
  • Sort order: asc (default) or desc
  • Task payloads include addedDate and changedDate (ISO 8601 or null)

Projects (11 tools)

  • CRUD — create, get, update, delete projects
  • Lifecycle — complete, uncomplete, set status (active/on-hold/dropped)
  • Organization — move between folders, search by name
  • Filtering — by folder, status, completion date range, stalled-only flag
  • Sorting — by name, due date, or other fields
  • Aggregate counts — project counts by status, optionally scoped to a folder

Project Lifecycle Semantics

  • Use complete_project when work is finished/closed (done/completed).
  • Use set_project_status for organizational state only:
    • active = current
    • on_hold = paused (UI wording is often "on hold"/"on-hold")
    • dropped = intentionally abandoned/cancelled, not completed
  • Use uncomplete_project to reopen a completed project back to active.
  • In user-facing summaries, present business meaning first (project name, folder, and status transition), and include opaque IDs only as secondary references.

Tags (5 tools)

  • CRUD — create, update (name and status), delete
  • List — with status filter (active/on-hold/dropped/all), sorting, and limits
  • Search — fuzzy name matching

Folders (5 tools)

  • CRUD — create, get (with child projects and subfolders), update, delete
  • Hierarchy — create nested folders with parent parameter
  • List — all folders with limits

Forecast (1 tool)

  • Structured view with sections: overdue, due today, flagged, deferred, and due this week

Perspectives (1 tool)

  • List all available OmniFocus perspectives

Resources (3)

Live snapshots available to MCP clients:

Resource Description
Inbox Current inbox tasks
Today Today's forecast (overdue + due today + flagged)
Active Projects All active projects with task counts

Prompts (4)

Ready-to-use review workflows:

Prompt Description
Daily Review Due-soon, overdue, and flagged tasks for daily planning
Weekly Review Active projects and next-action coverage analysis
Inbox Processing One-by-one inbox clarification decisions
Project Planning Guided planning for a specific project

Implementations

Three implementations with identical tool names, parameters, and response shapes:

Implementation Language Install Recommended For
Rust Rust Homebrew (recommended) or source Production use — single binary, fast startup
Python Python 3.11+ uv from source Local development, easy scripting
TypeScript Node.js 20+ npm from source Node.js ecosystems

Detailed setup guides: Rust · Python · TypeScript

How It Works

The server runs JXA (JavaScript for Automation) scripts through macOS osascript. Each script uses the OmniFocus evaluateJavascript bridge to execute Omni Automation JavaScript inside OmniFocus itself, where full APIs like flattenedTasks, Task.Status, and new Task() are available. Data is serialized as JSON and returned through the MCP protocol with consistent schemas across all three implementations.

MCP Client Config Examples

Claude Desktop

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

Cursor

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

Python (source build)

{
  "mcpServers": {
    "omnifocus": {
      "command": "uv",
      "args": ["run", "omnifocus-mcp"]
    }
  }
}

TypeScript (source build)

{
  "mcpServers": {
    "omnifocus": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/absolute/path/to/OmnifocusMCP/typescript"
    }
  }
}

Keep only one OmniFocus MCP server enabled at a time to avoid duplicate tool surfaces.

Compatibility snippet:

{
  "mcpServers": {
    "omnifocus": {
      "command": "python",
      "args": ["-m", "omnifocus_mcp"]
    }
  }
}
{
  "mcpServers": {
    "omnifocus": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/absolute/path/to/OmnifocusMCP/typescript"
    }
  }
}

Switching Implementations

Switching Between Rust, Python, and TypeScript

  • Use Rust when you want a single prebuilt omnifocus-mcp binary.
  • Use Python when you want uv or python -m execution and fast local iteration.
  • Use TypeScript when you want node execution from typescript/dist/index.js.
  • Restart the MCP client so it reloads the server command after you switch implementations.

Prerequisites

  • macOS (required — OmniFocus is macOS-only)
  • OmniFocus installed and running
  • Automation permission granted to the terminal/editor (System Settings → Privacy & Security → Automation)

For source builds only:

  • Python 3.11+ and uv (Python implementation)
  • Node.js 20+ and npm (TypeScript implementation)
  • Rust toolchain via rustup (Rust source build)

Contributing

Contributions are welcome through focused pull requests with clear scope and passing checks. See CONTRIBUTING.md for setup and validation steps.

License

MIT. See LICENSE for details.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选