MacOS Use MCP Server

Overview

The mcp‑server‑macos-use server brings the power of macOS accessibility to AI assistants via the Model Context Protocol (MCP). By exposing a set of high‑level tools that wrap native macOS control primitives, it allows Claude Desktop and other MCP clients to programmatically open applications, simulate user input, and introspect UI state—all without writing custom code for each platform. This eliminates the need for developers to craft separate automation scripts or rely on third‑party tools, streamlining the integration of macOS control into conversational AI workflows.

What Problem Does It Solve?

Traditional automation on macOS relies on AppleScript, UI scripting, or bespoke Swift code that is difficult to expose to an AI assistant. The server abstracts these complexities behind a simple, standardized MCP interface. Developers can now ask an AI to “open Safari and search for ‘MCP server’,” and the assistant translates that request into a series of MCP tool calls. The result is a seamless, end‑to‑end interaction where the AI can not only issue commands but also receive real‑time feedback about the UI, enabling dynamic decision making.

Core Capabilities

• Application Control – launches or activates any app by bundle ID, name, or path, then captures its accessibility tree.
• Input Simulation – , , and let the AI perform mouse clicks, type text, or press arbitrary keys (with modifiers) inside a target window.
• State Inspection – queries the current UI hierarchy without changing state, useful for polling or verification.
• Customizable Traversal – Optional parameters such as , , and give fine‑grained control over when and how the UI is inspected, allowing developers to tailor responses to specific needs.

Each tool returns a detailed snapshot of the accessibility tree after execution, enabling the AI to understand what changed and to plan subsequent actions.

Real‑World Use Cases

• Automated Testing – Generate test scenarios where the AI opens an app, navigates menus, and verifies UI elements, all captured through traversal diffs.
• Accessibility Audits – Inspect the accessibility tree of any macOS application to identify missing labels or roles, then report findings back to the user.
• Productivity Workflows – Build conversational shortcuts such as “compose an email in Mail” or “take a screenshot and upload it,” where the AI orchestrates multiple UI interactions.
• Educational Tools – Demonstrate macOS automation to learners by letting the AI narrate each step while it performs actions in real time.

Integration with AI Workflows

The server listens on standard input/output, making it a first‑class MCP client. An AI assistant can invoke any of the exposed tools via the method, passing parameters and optional flags as part of a JSON payload. Because traversal results are returned in the same format, the assistant can parse and present UI state directly to users or use it to refine future commands. The ability to include visual feedback animations () also helps users understand what the assistant is doing, improving transparency and trust.

Unique Advantages

• Native Swift Implementation – Leveraging the ensures high‑performance, low‑latency interactions with macOS’s accessibility APIs.
• Unified API Surface – All actions funnel through a single MCP method (), simplifying client code and reducing the learning curve.
• Extensibility – The optional parameters expose advanced traversal options without cluttering the core tool signatures, allowing future enhancements without breaking existing integrations.
• Cross‑Platform Consistency – While tailored for macOS, the server follows MCP conventions that are portable to other operating systems with their own tool sets, making it easier to build hybrid assistants.

By encapsulating complex macOS automation behind a concise MCP interface, the mcp‑server‑macos-use server empowers developers to create richer, more interactive AI experiences that can control native applications with precision and reliability.