🏭 Industry 4.0 Machine Health MCP Server

Bridging the gap between Industrial IoT Data and Conversational AI

📖 Table of Contents

• 🌟 Overview

• ⚠️ Problem Statement

• 💡 Solution & AI Integration

• 🔄 Architecture & Flow

• 📂 Project Structure

• 🛠️ Available MCP Tools

• 🚀 Getting Started (Local Setup)

• 🧪 Testing via NitroStudio

• ☁️ Deployment & ChatGPT Integration

• 🔮 Future Scope

• 🤝 Community & Links

Related MCP server: operational-data-mcp

🌟 Overview

The Industry 4.0 Machine Health MCP Server is a Model Context Protocol (MCP) based application built for the NitroStack Hackathon.

It empowers factory operators and managers to interact with complex industrial telemetry data using simple natural language via ChatGPT.

Instead of navigating through complex dashboards, a user can simply ask:

"What is the current temperature of Machine 1?"

And ChatGPT will fetch the real-time data through this MCP server.

⚠️ Problem Statement

In Industry 4.0 environments, factory machines generate telemetry data such as temperature, vibration, and RPM. In production, this would typically live in a time-series database like InfluxDB.

Today, this demo runs against an in-memory PlantDatabase in industry.data.ts, which means:

• Data access is already standardized through MCP Tools

• Non-technical users can query it through ChatGPT

• The same tool contract can later target a real time-series database without changing the AI workflow

💡 Solution & AI Integration

We created an MCP Server using the NitroStack SDK. This server exposes structured tools that ChatGPT can call directly, while all machine data is served from the in-memory PlantDatabase defined in src/modules/industry/industry.data.ts.

This keeps the AI layer decoupled from storage:

• MCP Tools define the contract

• PlantDatabase acts as the current data source

• A future InfluxDB connector can replace it without changing the AI workflow

🔄 Architecture & Flow

flowchart LR
    A["Factory Machines / IoT Sensors"] -->|Telemetry Data| B["PlantDatabase industry.data.ts"]
    B -->|In-Memory Mock Data| C["NitroStack MCP Server TypeScript"]
    C -->|"@Tool Functions"| D["NitroCloud Hosted Deployment"]
    D -->|Exposes Server URL| E["ChatGPT MCP Client"]
    E -->|Natural Language Query| F["End User"]

    style A fill:#ff9f43,color:#fff
    style B fill:#54a0ff,color:#fff
    style C fill:#5f27cd,color:#fff
    style D fill:#00d2d3,color:#fff
    style E fill:#10ac84,color:#fff
    style F fill:#feca57,color:#333

Data Flow

sequenceDiagram
    participant U as "User"
    participant C as "ChatGPT"
    participant S as "MCP Server"
    participant DB as "PlantDatabase"

    U->>C: "What is the health of MCH-001?"
    C->>S: Calls get_machine_health tool
    S->>S: Validates input with Zod
    S->>DB: Reads from industry.data.ts
    DB-->>S: Returns machine data
    S-->>C: JSON response
    C-->>U: "Machine MCH-001 is running at 72C..."

📂 Project Structure

industry4-mcp/
├── src/
│   ├── index.ts                       # Application bootstrap
│   ├── app.module.ts                  # Root application module
│   └── modules/
│       └── industry/                  # Industry 4.0 module
│           ├── industry.module.ts
│           ├── industry.tools.ts      # MCP Tools (get_machine_health)
│           ├── industry.prompts.ts    # Plant orchestrator prompt
│           └── industry.data.ts       # In-memory PlantDatabase
├── widgets/                           # NitroStudio UI Widgets (Next.js)
├── package.json                       # Dependencies (@nitrostack/core, zod)
└── .env                               # Environment variables

🛠️ Available MCP Tools

The server currently exposes the following tool to the AI:

get_machine_health

Input Schema (Zod)

{
  machine_id: z.string() // e.g., "MCH-001"
}

Response Format

{
  "machine_id": "MCH-001",
  "temperature": 72.5,
  "vibration_level": 0.45,
  "health_status": "healthy",
  "last_maintenance": "2026-07-15"
}

🚀 Getting Started (Local Setup)

Prerequisites

• 🟢 Node.js (v18+ required, v20.x recommended by NitroStack)

• 📦 npm or npx

Installation

# 1. Clone the repository
git clone https://github.com/AryanPROOO/industry4-mcp.git
cd industry4-mcp

# 2. Install dependencies
npm install

# 3. Start the development server
npm run dev

The server will start running locally on the default STDIO/HTTP port.

🧪 Testing via NitroStudio

NitroStudio is the official desktop IDE to test MCP servers before deploying them.

1. 📥 Download & Install — Get NitroStudio from nitrostack.ai/studio

2. 🔑 Sign In — Use your NitroCloud account

3. ➕ Add Server — Click Add Server → Select Nitro Project tab

4. 📁 Browse Project — Select the industry4-mcp folder

5. 🖥️ Open App Canvas — Navigate to the Studio App Canvas

6. 🔧 Test Tool — Go to Tools → Select get_machine_health

7. ▶️ Execute — Input MCH-001 and click Execute Tool

☁️ Deployment & ChatGPT Integration

Once the tool is working locally, it's time to make it live!

Step 1: Deploy to NitroCloud

1. In NitroStudio, click the Deploy button in the header

2. Follow the modal steps:

   • 📦 Preparing bundle

   • ⬆️ Uploading

   • 🔨 Building

   • ✅ Live

3. Copy your Service URL

Step 2: Connect to ChatGPT

1. Open ChatGPT (Plus/Pro account required)

2. Go to Settings → Plugins (Apps) and enable Developer Mode

3. Click the + (Add Plugin) button

4. Select Server URL as the connection type

5. Paste your Service URL and add /sse at the end:

   https://xyz.nitrocloud.app/sse

6. Click Create and then Connect

Step 3: Talk to your Factory! 🗣️

Try asking ChatGPT:

• 💬 "What is the health of machine MCH-001?"

• 💬 "Is machine 4 running hot?"

• 💬 "Which machines need maintenance?"

🔮 Future Scope

🤝 Community & Links

Built with ❤️ for the NitroStack Hackathon 2026

Empowering Industry 4.0 with Conversational AI