Manufacturing & Industry 4.0 Lirilli LarillaSubmitted July 26, 2026

industrial-process-workload-monitoring

An MCP app on the Model Context Protocol built by Lirilli Larilla at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

The Industrial Edge Machinery & Workload Health Monitoring System is an AI-driven, real-time telemetry and diagnostic platform engineered for smart manufacturing, industrial automation controllers (IPCs), edge computing nodes, and factory robotics. Built on the Model Context Protocol (MCP) using FastMCP and LangChain, the system enables autonomous AI agents and maintenance engineers to continuously monitor hardware compute health across CPU, RAM, and NVIDIA GPUs (auditing per-core load, thermal zones, VRAM, and power draw to prevent overheating or compute bottlenecks), inspect low-level Linux kernel ring buffers (`dmesg`) and OS system logs for hardware driver failures or Out-of-Memory crashes, and audit software logs from edge vision models and SCADA gateway processes. Upon detecting critical equipment anomalies or process failures, the platform automatically dispatches structured, root-cause incident reports via email to plant reliability engineers for proactive, predictive maintenance.

Manufacturing & Industry 4.0 track

Create intelligent systems for smart factories, predictive maintenance, quality control, and supply chain optimization.

Source code

Team Lirilli Larilla

  • Vishal RajaramanLead

  • Raam Prathap

  • Arjun R

  • Sakthi Karthik B C

← All Amrita University Coimbatore projects

Frequently asked questions

What does industrial-process-workload-monitoring do?
The Industrial Edge Machinery & Workload Health Monitoring System is an AI-driven, real-time telemetry and diagnostic platform engineered for smart manufacturing, industrial automation controllers (IPCs), edge computing nodes, and factory robotics. Built on the Model Context Protocol (MCP) using FastMCP and LangChain, the system enables autonomous AI agents and maintenance engineers to continuously monitor hardware compute health across CPU, RAM, and NVIDIA GPUs (auditing per-core load, thermal zones, VRAM, and power draw to prevent overheating or compute bottlenecks), inspect low-level Linux kernel ring buffers (`dmesg`) and OS system logs for hardware driver failures or Out-of-Memory crashes, and audit software logs from edge vision models and SCADA gateway processes. Upon detecting critical equipment anomalies or process failures, the platform automatically dispatches structured, root-cause incident reports via email to plant reliability engineers for proactive, predictive maintenance.
Who built industrial-process-workload-monitoring?
industrial-process-workload-monitoring was built by team Lirilli Larilla at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon, in the Manufacturing & Industry 4.0 track.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.