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

# 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)

python IoT_mcp_sim.py

Option 2: Production Mode (Real Hardware)

# 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 - a powerful framework for orchestrating MCP servers with AI agents.

Example: AI-Controlled Industrial System

#!/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)

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

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 <plc_ip>
• 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

# 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 - Simple and efficient way to interact with MCP servers using custom agents
• Model Context Protocol - Open protocol for tool integration with LLMs
• MQTT Protocol - Lightweight messaging protocol for IoT
• Modbus Protocol - Industrial communication protocol
• 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

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Designed for PolyMCP

Star ⭐ this repo if you find it useful!