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Pratyay/mac-monitor-mcp

by Various

🐍 🏠 🍎 - Identifies resource-intensive processes on macOS and provides performance improvement suggestions.

P

MCP

Pratyay/mac-monitor-mcp

Added 1 June 2026

Overview

Pratyay/mac-monitor-mcp is a Python-based MCP server that identifies resource-intensive processes on macOS and offers performance improvement suggestions. It runs locally and integrates with AI assistants via the Model Context Protocol, allowing natural language queries about system resource usage.

Best for

Best for
Developers who want conversational macOS performance diagnostics via AI assistants

Use cases

  • Ask an AI assistant to find processes using high CPU or memory on your Mac
  • Get performance tuning suggestions for specific applications
  • Monitor system health during development sessions

How to use

Install

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

Tools exposed

  • get_resource_intensive_processes
  • get_processes_by_category
  • get_system_overview

Notes

Pratyay/mac-monitor-mcp is a Python-based MCP server that identifies resource-intensive processes on macOS and offers performance improvement suggestions. It runs locally and integrates with AI assistants via the Model Context Protocol, allowing natural language queries about system resource usage.

21 stars on GitHub. Last updated 2026-03-30.

Use cases

  • Ask an AI assistant to find processes using high CPU or memory on your Mac
  • Get performance tuning suggestions for specific applications
  • Monitor system health during development sessions

Pros

  • Provides actionable, specific suggestions rather than just raw metrics
  • Lightweight Python implementation easy to install and configure
  • Integrates directly with MCP-compatible AI tools for conversational diagnostics

Cons

  • macOS only, no Linux or Windows support
  • Read-only analysis with no ability to kill or manage processes
  • Requires an MCP client (e.g., Claude Desktop) to be useful

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Provides actionable, specific suggestions rather than just raw metrics
  • Lightweight Python implementation easy to install and configure
  • Integrates directly with MCP-compatible AI tools for conversational diagnostics

Cons

  • macOS only, no Linux or Windows support
  • Read-only analysis with no ability to kill or manage processes
  • Requires an MCP client (e.g., Claude Desktop) to be useful

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

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