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mcp-remote-macos-use

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Basic Information

This repository provides an open-source MCP server that enables AI agents to fully control remote macOS systems. It is presented as a direct alternative to OpenAI Operator and is optimized for autonomous agents with complete desktop capabilities. The server is designed to be run in Docker and added as an MCP server in Claude Desktop or other MCP clients, allowing AI models to interact with a macOS machine without installing any background software on the target Mac. Control is achieved via built-in macOS Screen Sharing or optional WebRTC support through LiveKit for lower latency. The project exposes a set of MCP tools that perform screen capture and GUI input actions using environment variables for connection details. The README emphasizes minimal setup and universal macOS compatibility across versions.

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App URL
https://github.com/baryhuang/mcp-remote-macos-use
Github Repository
https://github.com/baryhuang/mcp-remote-macos-use/blob/main/README.md

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App Details

Features
Provides remote desktop control tools exposed as MCP functions including remote_macos_get_screen, remote_macos_send_keys, remote_macos_mouse_move, remote_macos_mouse_click, remote_macos_mouse_double_click, remote_macos_mouse_scroll, remote_macos_open_application and remote_macos_mouse_drag_n_drop. Deployable as a Docker image with instructions for building and publishing multi-platform images. Integrates with Claude Desktop via MCP configuration, supports WebRTC via LiveKit for low-latency streaming, and uses macOS Screen Sharing so no additional agent software is required on the target Mac. Notes no extra API costs for screen processing when used with Claude Pro. Uses environment variables for connection credentials. MIT-licensed and documents security and protocol limitations for authentication support.
Use Cases
Enables developers and researchers to run autonomous AI workflows that operate a real macOS desktop, useful for automating GUI tasks, demonstrations, or agent research without modifying target machines. The toolset lets an LLM view the screen and simulate keyboard and mouse interactions, open applications, and perform drag-and-drop, enabling end-to-end task execution on macOS. Examples mentioned include automating social media engagement and recruiting workflows. LiveKit support improves responsiveness for interactive use. Docker packaging and simple MCP integration let teams deploy quickly and begin testing desktop-capable agents while avoiding the need to install background services on remote Macs.

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