MacOS Resource Monitor MCP Server

Stop juggling between Activity Monitor, top, and ps when you need to understand what's eating your Mac's resources. This MCP server gives your AI assistant direct access to real-time system performance data - CPU hogs, memory leaks, and network chatterers - all in clean, structured JSON.

Why You Need This

You're deep in a debugging session when your MacBook starts sounding like a jet engine. Instead of alt-tabbing to Activity Monitor or dropping into terminal to run ps aux | head, ask your AI assistant: "What's killing my CPU right now?" Get instant, actionable data without breaking your flow.

The server uses macOS's native ps and lsof utilities under the hood, so there's no performance overhead from yet another monitoring daemon. It's a thin, intelligent layer that transforms command-line noise into structured data your AI can actually work with.

Real-World Scenarios

Performance Debugging: Your test suite suddenly takes 3x longer to run. Instead of manually hunting through processes, let your AI assistant identify that Docker Desktop is consuming 45% CPU and correlate it with your recent container updates.

Resource Optimization: Building deployment scripts? Have your AI continuously monitor resource usage during builds, automatically flagging when specific processes exceed thresholds or when memory usage patterns change.

Development Environment Health: Working on a resource-intensive project? Set up automated checks that alert you when background processes start competing with your development tools for system resources.

What You Get

The server exposes a single get_resource_intensive_processes() tool that returns comprehensive resource data:

{
  "cpu_intensive_processes": [
    {"pid": "1234", "cpu_percent": 45.2, "command": "node"},
    {"pid": "5678", "cpu_percent": 32.1, "command": "Docker"}
  ],
  "memory_intensive_processes": [
    {"pid": "1234", "memory_percent": 8.5, "resident_memory_kb": 1048576, "command": "Chrome"},
    {"pid": "8901", "memory_percent": 6.2, "resident_memory_kb": 768432, "command": "IntelliJ"}
  ],
  "network_intensive_processes": [
    {"command": "Slack", "network_connections": 12},
    {"command": "Spotify", "network_connections": 8}
  ]
}

This structured format means your AI can immediately identify patterns, suggest optimizations, or even help you script automated responses to resource issues.

Integration Into Your Workflow

Add this to your MCP-enabled AI setup and you gain system visibility without switching contexts. Ask questions like:

• "Which processes are using the most memory right now?"
• "Is anything unusual consuming CPU compared to yesterday?"
• "What's making all these network connections?"

Your AI assistant can now correlate system performance with your development activities, helping you optimize before problems become critical.

Quick Setup

git clone https://github.com/Pratyay/mac-monitor-mcp.git
cd mac-monitor-mcp
python -m venv venv && source venv/bin/activate
pip install mcp
python src/monitor.py

The server starts immediately and integrates with any MCP-compatible AI client. No configuration files, no complex setup - just real-time system insights through your existing AI workflow.

Perfect for developers who want system monitoring that actually fits into their development process rather than interrupting it.