MacOS Resource Monitor MCP Server

Name: MacOS Resource Monitor MCP Server
Author: Pratyay

@Pratyay

Repository

About MacOS Resource Monitor MCP Server

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mac-monitor-mcp": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "."
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MacOS Resource Monitor MCP Server?

MacOS Resource Monitor MCP Server is a Model Context Protocol server that identifies resource-intensive processes on macOS across CPU, memory, and network usage. It is designed for users who want to monitor system resources via an LLM or other MCP client.

How to use MacOS Resource Monitor MCP Server?

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().

Key features of MacOS Resource Monitor MCP Server

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.

Use cases of MacOS Resource Monitor MCP Server

System performance monitoring and bottleneck identification.
Resource usage trending and capacity planning.
Troubleshooting system slowdowns and performance issues.
Getting a quick system health overview via an LLM.
Automating resource analysis as part of a larger MCP workflow.

FAQ from MacOS Resource Monitor MCP Server

How does this server differ from macOS Activity Monitor?

It exposes data via the MCP protocol for LLM consumption rather than a graphical interface, and uses ps and lsof for real-time snapshots.

What are the runtime requirements?

macOS operating system, Python 3.10 or later, and the MCP server library (mcp).

How is the data collected and where does it come from?

Data is collected on each tool invocation using built-in macOS command-line utilities ps and lsof; no persistent storage is used.

Are there any known limits?

The get_resource_intensive_processes() tool only returns the top 5 processes per category. Network monitoring counts connections without measuring bandwidth.