"Remote desktop control and assistance tools" matching MCP servers:

Enables Claude Agent SDK agents to safely operate a remote user machine via a WebSocket tunnel, providing six client__* MCP tools with path fencing and phase-switch adjudication.

Enables remote desktop control with screen capture and mouse/keyboard simulation through an MCP server exposed via a WebSocket tunnel.

An MCP server that lets agents observe and control an Omarchy or Hyprland desktop: list monitors, windows, workspaces, and accessibility controls, capture screenshots, then move the pointer, type text, send shortcuts, and manage windows and workspaces. It works over stdio with any MCP client, guarding actions with session-lock and single-driver checks while treating on-screen content as untrusted data.

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.

Enables supervised, visible control of a Windows desktop from an MCP client, providing real mouse and keyboard input, screenshots, local safety controls, and a teaching mode for guidance without injection.

Windows desktop automation MCP server enabling screenshot capture, precise visual locating of UI elements via vision models, and automated mouse clicking.

Enables multiple local AI desktop applications to exchange MCP messages and collaborate through a shared JSONL room file, with role-based routing, history, and audit session tools.

Enables MCP clients to capture screenshots and control a Raspberry Pi's Wayland desktop over an existing SSH connection with mouse, drag, scroll, keyboard, and Unicode typing actions. Input tools return fresh screenshots, enforce single-client sessions with idle release, and reject stale or uncertain state until verified.

A governance and control layer for MCP tools that manages tool requests as intents through policy-based approval, queuing, or blocking. It enables secure human oversight and audit trails for consequential agent actions across platforms like Claude Desktop and Cursor.

Enables other agents to list, read, start, and steer Codex chats in the macOS ChatGPT desktop app, including sending follow-ups, choosing model/reasoning/speed, interrupting work, and answering approvals. It can run locally or behind bearer-token-protected tunnels for remote monitoring and control.

Enables LLMs to orchestrate AI agents and tasks, inspect DAG workflows, monitor cluster metrics, and perform safe database queries and statements through standardized MCP tools.

Enables durable bidirectional handoffs between any MCP client and OpenAI Codex Desktop tasks, with persistent callbacks, acknowledgements, and session-scoped state across restarts.

Enables AI agents to capture multi-monitor screenshots with coordinate grids and perform mouse, keyboard, scrolling, dragging, and window-management actions on Windows via MCP tools or CLI. It provides pixel-accurate desktop automation for computer-use agents.

MCP server that gives Claude Code full mouse, keyboard, and app control on macOS. Tell Claude what to do and watch it operate your computer.

Enables coding agents to request, track, and verify independent cross-model reviews with structured findings, attachment hashes, and provenance receipts via Chrome Bridge, Codex, or Responses transports.

Enables macOS computer use through the agent-desktop CLI: accessibility-tree snapshots with refs, headless clicks, typing, key presses, scrolling, screenshots, clipboard access, waits, and batch actions across 21 tools. Acts as a drop-in replacement for the bundled kimi-cu plugin, without moving the cursor or stealing focus for most operations.

Enables one coding agent to inspect, configure, debug, and repair another agent over stdio MCP, with redacted reads and confirm-gated writes and restarts.

Give any AI full control of your desktop. Mouse, keyboard, screenshots — all through the Model Context Protocol.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Give AI agents eyes and hands on macOS.

Reliable Codex Desktop automation for long tasks with durable async operations, Plan Mode workflows, and retry-safe task submission.

Enables Claude Code to read and operate macOS app interfaces through the accessibility tree, pressing buttons, setting text fields, typing with IME support, sending shortcuts, scrolling, dragging, and capturing screenshots. It drives background apps without moving the mouse or stealing focus from the user's current work.

Enables AI agents to observe and operate a real Windows or Linux desktop like a human — snapshotting screen geometry, active window, OCR text and normalized UI elements, then clicking, typing, scrolling and managing windows with built-in verification and recovery. Backends are chosen automatically per action (UIA/AT-SPI → OCR → vision → PyAutoGUI), so browsers, IDEs, office suites and canvas apps are all controllable without any browser-automation MCP.

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.

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

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.

This server enables AI agents to operate Windows and Linux computers by observing the screen, understanding the UI through accessibility, OCR, and vision, and performing mouse, keyboard, window, clipboard, and system actions with verification and recovery.

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

Enables AI assistants to observe, reason about, and control connected Android devices via ADB, providing tools for screenshots, UI hierarchy parsing, semantic element clicking, gestures, text input, and app lifecycle management.

Enables safe automation of Chrome browser through a local MCP server and Chrome extension, allowing LLMs to control browser tabs, pages, and computer-use actions with permission controls.