mac-monitor-mcp

MacOS Resource Monitor MCP Server


A Model Context Protocol (MCP) server that identifies resource-intensive processes on macOS across CPU, memory, and network usage.

Hosted deployment

A hosted deployment is available on Fronteir AI.

Overview

MacOS Resource Monitor is a lightweight MCP server that exposes an MCP endpoint for monitoring system resources. It analyzes CPU, memory, and network usage, and identifies the most resource-intensive processes on your Mac, returning data in a structured JSON format.

Requirements

• macOS operating system
• Python 3.10+
• MCP server library

Installation

Option 1: Global Installation (Recommended)

Install the MCP server globally using uv for system-wide access:

git clone https://github.com/Pratyay/mac-monitor-mcp.git
cd mac-monitor-mcp
uv tool install .

Now you can run the server from anywhere: ``bash mac-monitor ``

Option 2: Development Installation

1. Clone this repository:

   git clone https://github.com/Pratyay/mac-monitor-mcp.git
   cd mac-monitor-mcp

1. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  

1. Install the required dependencies:

   pip install mcp

Usage

Global Installation

If you installed globally with uv: ``bash mac-monitor ``

Development Installation

If you're running from the project directory: ``bash python src/mac_monitor/monitor.py ``

Or using uv run (from project directory): ``bash uv run mac-monitor ``

You should see the message: `` Simple MacOS Resource Monitor MCP server starting... Monitoring CPU, Memory, and Network resource usage... ``

The server will start and expose the MCP endpoint, which can be accessed by an LLM or other client.

Available Tools

The server exposes three tools:

1. get_resource_intensive_processes()

Returns information about the top 5 most resource-intensive processes in each category (CPU, memory, and network).

2. get_processes_by_category(process_type, page=1, page_size=10, sort_by="auto", sort_order="desc")

Returns all processes in a specific category with advanced filtering, pagination, and sorting options.

Parameters:

• process_type: "cpu", "memory", or "network"
• page: Page number (starting from 1, default: 1)
• page_size: Number of processes per page (default: 10, max: 100)
• sort_by: Sort field - "auto" (default metric), "pid", "command", or category-specific fields:
• CPU: "cpu_percent", "pid", "command"
• Memory: "memory_percent", "resident_memory_kb", "pid", "command"
• Network: "network_connections", "pid", "command"
• sort_order: "desc" (default) or "asc"

Example Usage: ```python

Get first page of CPU processes (default: sorted by CPU% descending)

get_processes_by_category("cpu")

Get memory processes sorted by resident memory, highest first

get_processes_by_category("memory", sort_by="resident_memory_kb", sort_order="desc")

Get network processes sorted by command name A-Z, page 2

get_processes_by_category("network", page=2, sort_by="command", sort_order="asc")

Get 20 CPU processes per page, sorted by PID ascending

get_processes_by_category("cpu", page_size=20, sort_by="pid", sort_order="asc") ```

3. get_system_overview()

Returns comprehensive system overview with aggregate statistics similar to Activity Monitor. Provides CPU, memory, disk, network statistics, and intelligent performance analysis to help identify bottlenecks and optimization opportunities.

Features:

• CPU Metrics: Usage percentages, load averages, core count
• Memory Analysis: Total/used/free memory with percentages
• Disk Statistics: Storage usage across all filesystems
• Network Overview: Active connections, interface statistics
• Performance Analysis: Intelligent bottleneck detection and recommendations
• System Information: macOS version, uptime, process count

Example Usage: ``python get_system_overview() # Get comprehensive system overview ``

Use Cases:

• System performance monitoring and analysis
• Identifying performance bottlenecks and slowdowns
• Resource usage trending and capacity planning
• Troubleshooting system performance issues
• Getting quick system health overview

Sample Output

get_resource_intensive_processes() Output

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

get_processes_by_category() Output

{
  "process_type": "cpu",
  "processes": [
    {
      "pid": "1234",
      "cpu_percent": 45.2,
      "command": "firefox"
    },
    {
      "pid": "5678",
      "cpu_percent": 32.1,
      "command": "Chrome"
    }
  ],
  "sorting": {
    "sort_by": "cpu_percent",
    "sort_order": "desc",
    "requested_sort_by": "auto"
  },
  "pagination": {
    "current_page": 1,
    "page_size": 10,
    "total_processes": 156,
    "total_pages": 16,
    "has_next_page": true,
    "has_previous_page": false
  }
}

How It Works

The MacOS Resource Monitor uses built-in macOS command-line utilities:

• ps: To identify top CPU and memory consuming processes
• lsof: To monitor network connections and identify network-intensive processes

Data is collected when the tool is invoked, providing a real-time snapshot of system resource usage.

Integration with LLMs

This MCP server is designed to work with Large Language Models (LLMs) that support the Model Context Protocol. The LLM can use the get_resource_intensive_processes tool to access system resource information and provide intelligent analysis.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Management Commands

If you installed the server globally with uv:

• List installed tools: uv tool list
• Uninstall: uv tool uninstall mac-monitor
• Upgrade: uv tool install --force . (from project directory)
• Install from Git: uv tool install git+https://github.com/Pratyay/mac-monitor-mcp.git

Recent Updates

Version 0.2.0 (Latest)

• ✅ Added get_processes_by_category() tool with pagination and sorting
• ✅ Added comprehensive sorting options (CPU%, memory, PID, command name)
• ✅ Added proper Python packaging with pyproject.toml
• ✅ Added global installation support via uv tool install
• ✅ Enhanced error handling and input validation
• ✅ Added pagination metadata with navigation information

Potential Improvements

Here are some ways you could enhance this monitor:

• Add disk I/O monitoring
• Improve network usage monitoring to include bandwidth
• Add visualization capabilities
• Extend compatibility to other operating systems
• Add process filtering by resource thresholds
• Add historical data tracking and trends