"How to Read Files Using a Computer" matching MCP servers:

MCP server for AI agents to control a Mac via screen and mouse/keyboard, supporting Anthropic Claude and OpenAI for task automation.

Exposes Anthropic's computer-use action surface (screenshot, click, move, keyboard, clipboard, batch) against a persistent desktop display via MCP stdio protocol. Enables AI agents to control a virtual desktop environment through natural language instructions.

Enables AI hosts to drive cross-platform desktop GUI automation and browser control, providing tools to read, click, type, send shortcuts, screenshot, and verify GUI elements and web pages.

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.

Provides a JSON-RPC computer use runtime for macOS, exposing 7 MCP tools (observe/act/inspect/session/cancel/trace) as image content blocks so external agents like Claude Code, Pi, OpenCode, or Codex CLI can capture screenshots and drive the desktop with clicks, keys, and typing while enforcing session locking, stale-frame protection, and trace redaction server-side.

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 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.

MCP server enabling AI agents to control a real Linux browser with live view, human takeover, and safety guardrails.

Enables AI agents to operate local desktops and Chromium browsers through MCP tools, unifying accessibility trees, physical input, screenshots, DOM/ARIA, visual grounding, and result verification.

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

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

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.

Enables LLMs to control Windows desktops via local OCR, optional YOLO icon detection, and mouse/keyboard actions.

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.

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 Claude Code to control a Windows desktop via screenshots, mouse, keyboard, and window/display enumeration, reusing the native binding from the Claude desktop app.

Provides an AI agent with a fixed, isolated Linux desktop workspace, enabling bounded screen capture, input control, and application launching via MCP.

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.

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