MCP 服务器

MacOS Resource Monitor MCP Server

Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.

README

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:

mac-monitor

Option 2: Development Installation

Clone this repository:

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

Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate

Install the required dependencies:
```
pip install mcp
```

Usage

Global Installation

If you installed globally with uv:

mac-monitor

Development Installation

If you're running from the project directory:

python src/mac_monitor/monitor.py

Or using uv run (from project directory):

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:

# 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:

 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.

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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