"Exploring Computer Usage" matching MCP servers:

Enables Claude Desktop to control mouse, keyboard, and clipboard on Windows, allowing AI-driven computer interaction tasks like clicking, typing, scrolling, and screenshotting.

Safety middleware for AI computer use agents that monitors, enforces policies, and provides guardrails to enable safe desktop interaction.

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

A framework-agnostic computer-use MCP server that exposes core desktop operations (screen capture, mouse, keyboard, and file access) as standard MCP tools, enabling any MCP-compatible agent to drive a computer.

Enables Windows desktop automation via MCP, allowing AI agents to control mouse, keyboard, and screen capture with the same interface as Anthropic's computer-use tool.

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

Enables autonomous agents to anchor browser elements from vision inputs, providing stable DOM references resilient to click drift for reliable computer-use automation.

Enhanced MCP server for full computer control: mouse, keyboard, screenshots, OCR, deep UI automation, semantic element actions, process management, filesystem watching, and accessibility-aware app launching.

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.

Aggregates and routes multiple MCP servers with intelligent tool recommendation and batch parallel execution, enabling unified access and efficient tool usage.

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 sandboxed computer-use MCP server that lets an AI agent drive a real browser and desktop apps, confined to a nested X11 window so it cannot touch your real screen or files.

Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.

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

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.

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

Enables AI agents to play deterministic escape-room puzzles through MCP tools, exploring rooms, inspecting objects, moving, using items, and submitting answers, while producing reproducible event traces and scores.

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

Enables agents to perform recorded, snapshot-evidenced computer-use and browser-use in a fresh, disposable VM, with interruptive actions judged by the agent.