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 to remotely control macOS systems via VNC with full desktop capabilities including screenshots, mouse control, keyboard input, and application management. Includes a web-based chat interface for natural language control.
Enables AI to dynamically discover and control native macOS applications (like Finder, Mail, Safari) through AppleScript/JXA automation without pre-built integrations.
macOS computer-use MCP server enabling AI agents to capture and interact with specific app windows via normalized coordinates, performing background clicks, keyboard input, and typing without taking over the physical cursor.
Enables coding agents to control other application windows by providing primitives for clicking, typing, capturing screenshots, and window management. Supports macOS, Windows, and Linux.
MCP server providing AI-friendly computer-use primitives (capture, detect, click) to let LLM agents drive desktop GUI applications on Windows, macOS, and Linux.
Enables localized macOS control by letting users grant temporary, window-scoped access for screen capture, pointer, keyboard, scrolling, clipboard, and Accessibility actions through a native host.
A compact facade MCP server for macOS desktop automation, lazily proxying to full-featured computer-use tools to save context tokens while enabling screenshots, window control, app launching, and AppleScript execution.
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.
Enables a macOS MCP client to control a real iPhone through CoreDevice services, including screenshots, normalized taps and swipes, text entry, hardware-button presses, and app launching or listing.
Local-first memory MCP server that captures macOS screen and app context into persistent Markdown, enabling any tool-capable LLM agent to query its working history.