MacOS Resource Monitor MCP Server
Description
# MacOS Resource Monitor MCP Server [](https://archestra.ai/mcp-catalog/pratyay__mac-monitor-mcp) A Model Context Protocol (MCP) server that identifies resource-intensive processes on macOS…
About
# MacOS Resource Monitor MCP Server [](https://archestra.ai/mcp-catalog/pratyay__mac-monitor-mcp) A Model Context Protocol (MCP) server that identifies resource-intensive processes on macOS across CPU, memory, and network usage…
Details
- Author
- Pratyay
- GitHub stars
- 22
- Downloads
- 196
- Categories
- Other
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- Lists top 5 most resource-intensive processes per category.
- Provides paginated, sortable process listing for CPU, memory, network.
- Returns comprehensive system overview with CPU, memory, disk, network stats.
- Uses built-in macOS utilities (ps, lsof) for real-time snapshots.
- Designed for LLM integration via the Model Context Protocol.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MacOS Resource Monitor MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install globally with uv tool install . or run from source with python src/mac_monitor/monitor.py. Then start the server, which exposes three tools: get_resource_intensive_processes(), get_processes_by_category(), and get_system_overview().
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"macos resource monitor mcp server": {
"mac-monitor-mcp": {
"command": "uv",
"args": [
"tool",
"install",
"."
]
}
}
}
}McpServers
{
"mac-monitor-mcp": {
"command": "uv",
"args": [
"tool",
"install",
"."
]
}
}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
1. Clone this repository:
git clone https://github.com/Pratyay/mac-monitor-mcp.git
cd mac-monitor-mcp
2. Create a virtual environment (recommended):
python -m venv venv
source venv/bin/activate
3. 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:
```python
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