"MobX-State-Tree" matching MCP servers:

Enables AI agents to operate a real GNOME Wayland desktop through accessibility-tree widget actions, pointer/keyboard input, OCR, window management, and screen capture, optionally on a private headless session.

Gives an LLM agent control of the macOS desktop from a single self-contained Rust binary: Set-of-Mark numbered screenshots, Accessibility-tree clicks with browser-JS fallback for web content, Apple Vision OCR, mouse, keyboard, and clipboard.

An MCP server that allows AI agents to drive real Android devices via adb, capturing screenshots, reading the live UI tree, and performing actions like tap, swipe, and type.

Provides a local MCP bridge for AI assistants to control a Mac by observing screen state and performing actions like mouse movement, clicking, typing, and opening URLs.

An MCP server that enables AI agents to capture targeted screenshots of specific application windows on Windows and Linux, with smart window state restoration and focus management.

An enterprise-grade automation server that enables AI assistants to control Windows PCs through intelligent UI element detection, window management, and system-level commands. It leverages the Windows UI Automation tree for reliable interaction, providing tools for mouse/keyboard control, application management, and high-performance screen state analysis.

State-aware Windows automation MCP server for AI agents, enabling app control, UI interaction, COM Office, and more via 59 tools with fallback layers and audit logging.

Enables interaction with a persistent PowerShell session through LM Studio. Provides tools to run commands, manage directories, and handle environment variables while maintaining session state between calls.

Enables MCP clients to start and stop local read-only Windows 11 screen observation sessions, then inspect redacted screen state, UI trees, frames, and wait for changes, title matches, or idle periods through stdio tools.

Wraps OpenAI Codex's computer-use engine to provide MCP tools for Windows screen operations including screenshot, UI automation tree, and input simulation, enabling Claude Code to control the desktop.

Lets local AI agents drive an Omarchy desktop by exposing its command registry and shell IPC targets over MCP, enabling command search, execution, and desktop state control.

Enables text-based control of Android devices through Claude Code using accessibility tree automation. Supports UI interaction, app launching, text input, gestures, and navigation without requiring screenshots.

A Model Context Protocol server for ConnectWise Automate with decision tree architecture, enabling Claude to manage scripts, computers, groups, and automation workflows.

Provides advanced iTerm2 terminal automation using Python API integration, enabling AI assistants to create and manage terminal sessions, split panes, execute commands, broadcast to multiple panes, and monitor terminal state in real-time.

A local daemon for macOS that gives any MCP-capable AI client eyes, hands, and a face — screen capture, accessibility tree, mouse/keyboard actions, and an overlay — with a built-in security ceiling.

MCP server that captures a Windows application's pixels and bounded UI Automation tree with provider-exposed text for Codex inspection. It supports capture, listing, retrieval, and watcher control commands.

Enables AI agents to see, locate UI elements, and operate any Windows desktop app through natural language, using accessibility-tree matching with optional vision-model fallback, plus an autonomous visual loop with introspection and meta-learning.

Enables MCP clients to control a Linux/X11 desktop like a human: see the screen, move the mouse, click UI elements via the accessibility tree, type text, and manage windows.

Enables AI agents to control Windows systems using AutoHotkey v2 and the UI Automation accessibility tree for efficient, text-based computer interaction. It provides tools for window management, keystrokes, and UI inspection while significantly reducing token costs compared to screenshot-based approaches.

Provides LLM clients with safe, persistent SSH access to remote machines through the Model Context Protocol. Maintains shell sessions that preserve environment state between commands, enabling multi-step workflows and interactive diagnostics on remote systems.