"How to Use MacOS" matching MCP servers:

The first open-source MCP server that enables AI to fully control remote macOS systems.

Bridges Kimi Code CLI to OpenAI Computer Use, enabling Kimi to control local macOS applications via MCP.

Restores OpenAI Codex Computer Use on macOS 13 by building a clean-room MCP backend that adapts to the bundled sky interface, enabling screen interaction and app control without modifying system libraries.

Standalone MCP server that gives AI agents full GUI control over macOS — screenshots, mouse, keyboard, apps, clipboard, and multi-display — with zero private dependencies.

Enables any compatible MCP harness to control macOS apps through OpenAI's Computer Use backend, with explicit approval checks.

An ultra-fast native MCP server for macOS desktop automation, enabling visual OCR, text-based clicking, window management, and keyboard/mouse control without hijacking the physical cursor.

Enables AI agents to anchor visible on-screen elements to stable DOM targets, preventing click drift during browser computer-use automation.

MCP server providing 200x faster macOS computer use via Accessibility API, enabling direct UI automation (click, type, scroll) with ~1s workflows.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables MCP-speaking clients to control a real desktop via the computer_use tool, including clicking, typing, scrolling, dragging, key combos, app focus, and screen/accessibility capture. It also provides a verdict system that verifies whether input actions had their intended effect, with configurable approval modes.

A deterministic Mastermind-style cipher lock exposed as MCP tools for benchmarking LLM reasoning and tool-use through a legible duel.

Self-learning macOS automation MCP server that remembers successful patterns and avoids repeated failures, enabling AI agents to execute multi-step workflows via reusable recipes.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

A macOS utility that captures screenshots and analyzes them with AI vision, enabling AI assistants to see and interpret what's on your screen.

Enables AI agents to drive a Linux/X11 desktop via AT-SPI accessibility-tree perception and element-level actions, avoiding screenshots and coordinate clicking.

Provides safe Linux desktop automation for Codex through focused MCP tools for inspecting, clicking, typing, dragging, and verifying native applications on Wayland and X11.

An open-source MCP server that lets any coding agent operate your computer like a person does—reading the UI through accessibility trees, clicking and typing in the background, showing an agent pointer, zooming into regions, and driving your signed-in Chrome on macOS, Windows, and Linux.

Enables Codex to delegate coding tasks to an OpenCode CLI locally, returning structured results such as exit codes, session summaries, tool calls, and git diffs.

A standalone MCP server for Linux desktop control, enabling screenshots, mouse/keyboard input, app/window management, and clipboard access via a Python bridge.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.