AI Agent for Remote macOS
Leverage the power of the first open-source MCP server for seamless AI-driven remote Mac control. Unlock full desktop automation on any macOS system without extra software installations. Enjoy universal compatibility, zero setup, and advanced screen sharing, empowering your AI agents to operate natively on macOS for tasks like recruiting, social engagement, and creative workflows.
Native macOS Control — No Extra Software
Remotely manage and automate any macOS desktop through AI agents using Model Context Protocol (MCP). Enable screen sharing and instantly empower your AI to take screenshots, send keyboard input, and control mouse actions on any Mac—no background apps or developer tools required.
- Universal Compatibility.
- Works with all macOS versions—current and future—so your remote workflows are never interrupted.
- Full Desktop Automation.
- AI agents can move the mouse, send keystrokes, take screenshots, and open applications just like a real user.
- Zero Setup Required.
- Just enable Screen Sharing on your Mac—no additional software or background agents needed.
- Open Architecture.
- Integrates seamlessly with any MCP Client and supports all major LLM providers for future-proof AI automation.
Real-Time, Low-Latency Screen Sharing
Experience smooth, high-performance remote Mac control powered by built-in WebRTC support via LiveKit. Benefit from low-latency screen sharing, automatic quality adaptation, and optimal network efficiency—giving your AI agent real-time access and control as if it were locally on the device.
- WebRTC Integration.
- Leverage LiveKit for high-quality, real-time screen sharing and enhanced remote performance.
- Automatic Quality Scaling.
- Dynamic quality adaptation ensures a smooth experience even in variable network environments.
- Optimized for Responsiveness.
- Low-latency connections keep your AI agent in sync with the remote Mac desktop in real time.
Powerful Remote Tools for Full Automation
Gain access to a full suite of robust remote control tools: take screenshots, send keystrokes, move and click the mouse, launch applications, and even perform drag-and-drop operations. Designed for AI agents, all tools work seamlessly via environment variables, with no manual configuration needed.
- Comprehensive Toolset.
- Includes screenshot capture, keyboard input, mouse control, app launching, and drag-and-drop for end-to-end automation.
- Secure Connections.
- Employs protocol 30 authentication and Diffie-Hellman key exchange for safe, encrypted remote access.
MCP INTEGRATION
Available Remote MacOs Use MCP Integration Tools
The following tools are available as part of the Remote MacOs Use MCP integration:
- remote_macos_get_screen
Connect to a remote macOS machine and capture a screenshot of the desktop.
- remote_macos_send_keys
Send keyboard input to a remote macOS machine for automated typing and shortcuts.
- remote_macos_mouse_move
Move the mouse cursor to specific coordinates on a remote macOS system.
- remote_macos_mouse_click
Perform a mouse click at given coordinates on a remote macOS machine.
- remote_macos_mouse_double_click
Perform a mouse double-click at specific coordinates on a remote macOS system.
- remote_macos_mouse_scroll
Scroll at designated coordinates to simulate mouse wheel movement on a remote macOS.
- remote_macos_open_application
Open or activate a specified application on the remote Mac and return its process ID.
- remote_macos_mouse_drag_n_drop
Perform a drag and drop mouse operation from a start point to an endpoint on macOS.
Connect Your Remote macOS AI Agent with FlowHunt AI
Connect your Remote macOS AI Agent to a FlowHunt AI Agent. Book a personalized demo or try FlowHunt free today!
What is MCP Remote macOS Control Server
MCP Remote macOS Control Server is the first open-source solution that empowers AI agents to fully control remote macOS systems through a Python-based MCP server. The platform features a modern web-based AI chat interface, which allows users and agents to issue natural language commands for direct manipulation of a Mac system via VNC. Designed as a direct alternative to OpenAI Operator, the solution offers both a robust backend (Node.js/Express) and a React-based frontend, supporting seamless real-time interactions and automation. It requires minimal setup—just enable Screen Sharing on any Mac you wish to control, with no extra software or agents needed. Supporting universal LLM compatibility and integration with providers like OpenAI and Anthropic, MCP Remote macOS Control Server is built for flexibility and future-proofing, enabling effortless deployment, secure operation, and instant productivity for research, recruiting, marketing, and more.
Capabilities
What we can do with MCP Remote macOS Control Server
With MCP Remote macOS Control Server, users and AI agents can remotely operate any macOS system via a secure, chat-driven web interface. The platform supports direct desktop manipulation, workflow automation, and integration with large language models for intelligent task execution.
- Remote Desktop Control
- Seamlessly control a remote Mac as if you were sitting in front of it, using natural language commands.
- Automate Tasks
- Use AI to automate repetitive or complex workflows such as launching apps, managing files, or browsing the web.
- Real-Time Interaction
- Engage in real-time chat with the AI agent to request screenshots, open applications, or perform system actions.
- Secure Access
- Connect to your own or authorized Macs using secure VNC and environment-based authentication.
- Universal LLM Integration
- Integrate with any major LLM provider (OpenAI, Anthropic, etc.) for advanced AI-driven operation.
What is MCP Remote macOS Control Server
AI agents can leverage MCP Remote macOS Control Server to achieve full desktop autonomy on macOS machines. This enables advanced use cases such as automated research, candidate screening, creative content generation, and marketing engagement—all executed with natural language instructions, robust security, and minimal setup. The architecture ensures reliable, real-time operation and future extensibility, making it ideal for deploying intelligent agents at scale.