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https://github.com/poly-mcp/IoT-Edge-MCP-Server

MCP server for Industrial IoT, SCADA and PLC systems. Unifies MQTT sensors, Modbus devices and industrial equipment into a single AI-orchestrable API. Features real-time monitoring, alarms, time-series storage and actuator control.
https://github.com/poly-mcp/IoT-Edge-MCP-Server

ai-agents ai-tools api-server automation industrial-iot llm mcp modbus model-context-protocol mqtt plc polymcp python real-time-monitoring scada

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MCP server for Industrial IoT, SCADA and PLC systems. Unifies MQTT sensors, Modbus devices and industrial equipment into a single AI-orchestrable API. Features real-time monitoring, alarms, time-series storage and actuator control.

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# IoT/Edge MCP Server



**Model Context Protocol (MCP) server for Industrial IoT, Edge Computing and Automation**



This server transforms industrial infrastructure into an AI-orchestrable system, exposing 11 powerful tools for complete IoT/SCADA/PLC control via HTTP endpoints. Perfect for AI-driven industrial automation, predictive maintenance, and smart factory operations.



## ✨ Features



- **🏭 Multi-Protocol Support** - Unified interface for industrial systems:

  - MQTT for wireless IoT sensors and actuators

  - Modbus TCP/RTU for PLC and industrial devices

  - Time-series data with InfluxDB integration

  - Real-time caching with Redis

  - Simulated mode for testing without hardware



- **📊 Complete Sensor Management** - Monitor and analyze industrial data:

  - Real-time sensor readings (temperature, pressure, flow, etc.)

  - Historical data with aggregations (mean, max, min)

  - Multi-sensor batch operations

  - Quality indicators and signal monitoring



- **⚡ Actuator Control** - Command industrial equipment:

  - Valve control (open/close)

  - Motor management (start/stop/speed)

  - Pump operations

  - PLC register manipulation



- **🚨 Alarm System** - Enterprise-grade monitoring:

  - Multi-priority alarms (LOW, MEDIUM, HIGH, CRITICAL)

  - Automatic threshold monitoring

  - Alarm acknowledgment tracking

  - Real-time notifications



- **🔒 Production Features** - Enterprise-ready:

  - Rate limiting for command safety

  - Comprehensive error handling

  - Connection pooling and auto-reconnect

  - Full audit logging

  - Thread-safe operations



## 🚀 Quick Start



### Installation



```bash

# Clone the repository

git clone https://github.com/llm-use/iot-mcp-server.git

cd iot-mcp-server



# Install dependencies

pip install -r requirements.txt



# For testing without hardware, use simulation mode

pip install -r requirements-sim.txt

```



### Starting the Server



**Option 1: Simulation Mode (No Hardware Required)**

```bash

python IoT_mcp_sim.py

```



**Option 2: Production Mode (Real Hardware)**

```bash

# Configure your devices in iot_config.yaml

# Start required services (MQTT, InfluxDB, Redis)

docker-compose up -d



# Start the server

python IoT_mcp.py

```



Server will start on `http://localhost:8000`



## 🤖 Using with PolyMCP



This MCP server is designed to work seamlessly with **[PolyMCP](https://github.com/llm-use/Polymcp)** - a powerful framework for orchestrating MCP servers with AI agents.



### Example: AI-Controlled Industrial System



```python

#!/usr/bin/env python3

import asyncio

from polymcp.polyagent import UnifiedPolyAgent, OllamaProvider



async def main():

    # Initialize your LLM provider

    llm = OllamaProvider(model="gpt-oss:120b-cloud", temperature=0.1)

    

    # Connect to IoT MCP server

    agent = UnifiedPolyAgent(

        llm_provider=llm, 

        mcp_servers=["http://localhost:8000/mcp"],  

        verbose=True

    )

    

    async with agent:

        print("✅ IoT MCP Server connected!\n")

        

        # Chat with your AI to control the industrial system

        while True:

            user_input = input("\n🏭 You: ")

            

            if user_input.lower() in ['exit', 'quit']:

                break

            

            result = await agent.run_async(user_input, max_steps=5)

            print(f"\n🤖 System: {result}")



if __name__ == "__main__":

    asyncio.run(main())

```



### Example Commands



Once connected, you can ask the AI agent to:



- *"Check the status of all temperature sensors and report any anomalies"*

- *"What's the average pressure in tank 1 over the last 6 hours?"*

- *"Open the main valve and start pump 1"*

- *"Show me all critical alarms that haven't been acknowledged"*

- *"If temperature exceeds 50°C, activate cooling system"*

- *"Generate an end-of-shift report with key metrics"*

- *"Monitor vibration sensor and alert if it exceeds normal range"*

- *"Optimize energy consumption by analyzing motor usage patterns"*



**That's it!** PolyMCP handles all the complexity of:

- Tool discovery and selection

- Multi-step industrial process automation

- Real-time monitoring and alerting

- Complex decision logic implementation



## 📡 API Endpoints



Once the server is running, you can access:



- **API Documentation**: `http://localhost:8000/docs`

- **List All Tools**: `http://localhost:8000/mcp/list_tools`

- **Invoke Tool**: `POST http://localhost:8000/mcp/invoke/{tool_name}`



## 🛠️ Available Tools



View all available tools (11 tools)



### Sensor Operations

- **`read_sensor`** - Read current value from a single sensor

- **`read_multiple_sensors`** - Batch read multiple sensors

- **`get_sensor_history`** - Retrieve historical data with optional aggregation



### Actuator Control

- **`execute_actuator_command`** - Send commands to actuators (valves, motors, pumps)



### System Management

- **`get_device_topology`** - View complete system architecture

- **`list_devices`** - List all registered devices with status

- **`get_system_status`** - Overall system health and statistics



### Alarm Management

- **`get_active_alarms`** - View active alarms by priority

- **`acknowledge_alarm`** - Confirm alarm acknowledgment



### PLC Operations

- **`read_modbus_registers`** - Read PLC registers via Modbus

- **`write_modbus_register`** - Write values to PLC registers



## 🔧 Configuration



### Basic Configuration (`iot_config.yaml`)



```yaml

mqtt:

  broker: "localhost"

  port: 1883

  username: "iot_user"

  password: "secure_password"



modbus:

  devices:

    - device_id: "plc_01"

      name: "Main PLC"

      type: "tcp"

      host: "192.168.1.100"

      port: 502



influxdb:

  url: "http://localhost:8086"

  token: "your-token"

  org: "iot"

  bucket: "sensors"



redis:

  host: "localhost"

  port: 6379

```



### Environment Variables



```bash

export MQTT_BROKER="broker.hivemq.com"

export INFLUX_TOKEN="your-token"

export REDIS_HOST="localhost"

```



## 📋 Requirements



**For Production Mode:**

- Python 3.8+

- MQTT Broker (Mosquitto, EMQX, HiveMQ, or any MQTT 3.1.1/5.0 broker)

- InfluxDB 2.0+ (for time-series data)

- Redis (for caching)

- Industrial devices (PLCs with Modbus, IoT sensors)



**For Simulation Mode:**

- Python 3.8+

- No external dependencies!



## 🐛 Troubleshooting



**Can't connect to MQTT?**

- Check broker is running: `mosquitto_sub -h localhost -t '#'`

- Verify credentials in config

- Check firewall settings



**Modbus connection failed?**

- Ensure PLC is accessible: `ping `

- Verify Modbus is enabled on device

- Check port (usually 502 for TCP)



**No sensor data?**

- Check MQTT topics match configuration

- Verify sensor is publishing data

- Look at Redis cache: `redis-cli get sensor:*`



## 🏗️ Architecture



```

┌─────────────┐     ┌─────────────┐     ┌─────────────┐

│ IoT Sensors │────▶│  MQTT       │────▶│             │

└─────────────┘     │  Broker     │     │             │

                    └─────────────┘     │   IoT MCP   │     ┌─────────────┐

┌─────────────┐     ┌─────────────┐     │   Server    │────▶│  PolyMCP    │

│    PLCs     │────▶│  Modbus     │────▶│             │     │  AI Agent   │

└─────────────┘     └─────────────┘     │             │     └─────────────┘

                                        │             │

┌─────────────┐                         │             │     ┌─────────────┐

│  InfluxDB   │◀────────────────────────│             │────▶│   Redis     │

└─────────────┘                         └─────────────┘     └─────────────┘

```



## 🤝 Contributing



Contributions are welcome! This project demonstrates industrial IoT integration with MCP protocol.



### Development Setup



```bash

# Clone repo

git clone https://github.com/llm-use/iot-mcp-server.git



# Create virtual environment

python -m venv venv

source venv/bin/activate  # or venv\Scripts\activate on Windows



# Install dev dependencies

pip install -r requirements-dev.txt



# Run tests

pytest tests/

```



## 📝 License



MIT License - See LICENSE file for details



## 🔗 Related Projects



- **[PolyMCP](https://github.com/llm-use/Polymcp)** - Simple and efficient way to interact with MCP servers using custom agents

- **[Model Context Protocol](https://modelcontextprotocol.io/)** - Open protocol for tool integration with LLMs

- **[MQTT Protocol](https://mqtt.org/)** - Lightweight messaging protocol for IoT

- **[Modbus Protocol](https://modbus.org/)** - Industrial communication protocol

- **[Mqttcpp](https://github.com/JustVugg/Mqttcpp)** - A lightweight and fast C++ library for building MQTT clients and brokers



## 💡 Why This Project?



This MCP server bridges the gap between **Industrial IoT** and **AI agents**. With PolyMCP, you can:



1. **Natural Language Control** - "Check all pressure sensors and alert if any are abnormal"

2. **Complex Automation** - AI can orchestrate multi-step industrial processes

3. **Predictive Maintenance** - AI analyzes trends and predicts failures

4. **Energy Optimization** - AI optimizes equipment usage for efficiency

5. **Incident Response** - AI handles alarms and executes emergency procedures



No complex industrial protocols to learn - PolyMCP and AI handle everything!



## 🚀 Use Cases



- **Smart Factory** - AI-driven production line optimization

- **Building Automation** - Intelligent HVAC and lighting control

- **Energy Management** - Real-time consumption optimization

- **Predictive Maintenance** - Equipment failure prediction

- **Quality Control** - Automated anomaly detection

- **Emergency Response** - AI-managed incident handling



---



**Designed for [PolyMCP](https://github.com/llm-use/Polymcp)**



*Star ⭐ this repo if you find it useful!*