Pratyay/mac-monitor-mcp

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:
Now you can run the server from anywhere:
Option 2: Development Installation
1. Clone this repository:
2. Create a virtual environment (recommended):
3. Install the required dependencies:
Usage
Global Installation
If you installed globally with uv:
Development Installation
If you're running from the project directory:
Or using uv run (from project directory):
You should see the message:
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:**
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:**
**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