MCP Glances Monitor

Example Use Cases

• 1

  Fetch real-time server metrics (CPU, memory, network, processes) from Glances endpoints and summarize system health.

• 2

  LLM-driven analysis and human-friendly explanations or diagnostics of monitoring data to assist in incident triage.

• 3

  Integrate remote server monitoring into MCP-based workflows or client environments like Cursor and Cline.

Description

This project implements an MCP Python service that queries Glances (a system monitoring tool) via its web API and uses a large language model to analyze and present the results. It is built with the MCP Python SDK and has been tested with Cursor and Cline clients for local integration. The README includes setup and configuration guidance for both monitored servers (Glances) and local MCP client integration, plus common error handling and best-practice notes. It helps operators get LLM-driven summaries and diagnostics of server health from Glances endpoints.