mcpserver.so LogoMCPFly
Submit
MacOS Resource Monitor MCP Server logo

MacOS Resource Monitor MCP Server

by Pratyay

DevOps/Monitoring & ObservabilitymacOSresource monitoringsystem resources

A lightweight MCP server that identifies resource-intensive processes on macOS across CPU, memory, and network usage. It exposes an MCP endpoint for monitoring system resources and returns data in a structured JSON format.

View on GitHub

Last updated: N/A

What is MacOS Resource Monitor MCP Server?

The MacOS Resource Monitor is a lightweight Model Context Protocol (MCP) server designed to monitor system resources on macOS. It identifies the most resource-intensive processes based on CPU, memory, and network usage, providing data in a structured JSON format.

How to use MacOS Resource Monitor MCP Server?

  1. Clone the repository. 2. Create and activate a virtual environment. 3. Install the required dependencies using pip install mcp. 4. Start the server using python src/monitor.py. The server exposes an MCP endpoint that can be accessed by an LLM or other client using the get_resource_intensive_processes() tool.

Key features of MacOS Resource Monitor MCP Server

  • Monitors CPU usage

  • Monitors memory usage

  • Monitors network usage

  • Identifies resource-intensive processes

  • Exposes an MCP endpoint

  • Returns data in JSON format

  • Integration with LLMs

Use cases of MacOS Resource Monitor MCP Server

  • System performance analysis

  • Identifying resource bottlenecks

  • Integration with LLMs for intelligent analysis

  • Troubleshooting performance issues

  • Automated resource management

FAQ from MacOS Resource Monitor MCP Server

What operating systems are supported?

Currently, only macOS is supported.

What is the Model Context Protocol (MCP)?

MCP is a protocol that allows Large Language Models (LLMs) to interact with external tools and services.

How does the server identify resource-intensive processes?

The server uses built-in macOS command-line utilities like ps and lsof to collect resource usage data.

Can I contribute to this project?

Yes, contributions are welcome! Please submit a Pull Request.

What are some potential improvements?

Potential improvements include adding disk I/O monitoring, improving network usage monitoring, adding visualization capabilities, and extending compatibility to other operating systems.