Supercharging IoT with Agentic Systems: The Model Context Protocol (MCP)
TL;DR
The Model Context Protocol (MCP) enables AI agents to directly interact with IoT platforms, transforming passive monitoring systems into autonomous decision-makers. You can try the Cumulocity IoT open-source MCP Server implementation, allowing LLMs like Claude and GPT to directly query and control your IoT devices. It’s early days—we want your feedback and contributions!
The Problem: Smart Devices, Dumb Interactions
Let’s face it—most IoT systems today are essentially glorified data collectors. Sure, they’re “connected,” but they operate in predefined patterns:
if (sensor_reading > threshold) {
trigger_alarm();
// Wait for a human to figure out what to do
}
Our control systems might be sophisticated, but the way we interact with them remains frustratingly primitive. We’ve built amazing IoT platforms that manage millions of devices, but then we force humans to be the integration layer between these systems and the broader business context.
Enter Agentic Systems: IoT’s Secret Weapon
Agentic systems represent a fundamental upgrade to traditional workflow automation. These AI-powered systems aren’t just following rules—they have a LLM as a brain so that they can be perceiving, deciding, and acting with autonomy.
# Traditional IoT approach
if temperature > 90:
alert_operator()
# Agentic approach
context = {
"device_history": get_maintenance_records(),
"weather_forecast": get_forecast(),
"production_schedule": get_schedule(),
"inventory": get_spare_parts()
}
recommended_action = agent.run(sensor_data, context)
execute(recommended_action) # E.g. trigger a work order with relevant context (e.g. link to spare part for ordering)
What makes an agent different from regular software? Three key characteristics:
- Context-awareness: Agents understand the broader environment they operate in
- Learning capability: They improve based on outcomes and feedback
- Autonomous decision-making: They can evaluate options and take appropriate actions
The Missing Link: Model Context Protocol (MCP)
With many companies developing their own agentic systems, integration with many different systems has become a bottle neck. Large Language Models (LLMs) like Claude and GPT are incredibly powerful, but getting them to work directly with IoT platforms requires custom coding and instruction for every new use case.
That’s where MCP comes in.
┌──────────────────┐ ┌──────────────┐ ┌────────────────────┐
│ LLM Agent │◄───┤ MCP Protocol │◄───┤ IoT Platform │
│ (e.g. Claude/GPT)│ │ Server │ │ (e.g. Cumulocity) │
└──────────────────┘ └──────────────┘ └────────────────────┘
The Model Context Protocol creates a standardized way for AI agents to discover and use external tools—in our case, IoT platform capabilities. Think of it as an API spec on steroids: it doesn’t just define endpoints but provides semantic understanding of what each tool does, when to use it, and how its outputs should be interpreted.
MCP enables:
- Dynamic discovery: Agents can automatically learn what capabilities exist
- Semantic understanding: Tools include descriptions of their purpose and context
- Standardized interaction: Consistent patterns for authentication, data retrieval, and actions
Real-World Example: The Wind Farm Revolution
Let’s make this concrete with an example. Imagine a wind farm, now powered by MCP:
-
Early Anomaly Detection: One morning, turbine #12 begins exhibiting subtle vibration patterns in its gearbox - far too nuanced for traditional threshold-based monitoring to flag as concerning, but potentially indicative of early bearing wear.
-
Contextual Analysis: The agent doesn’t just see the anomaly in isolation. Using MCP, it pulls maintenance history from the CMMS system, weather forecast data from meteorological services, and power production schedules from the energy management system. It discovers that:
- Similar vibration patterns preceded three previous gearbox failures in the fleet
- A cold front with high winds is expected in 72 hours
- The affected turbine is scheduled for peak production during the upcoming high-demand period
-
Intelligent Decision-Making: Rather than simply alerting a technician, the agent evaluates multiple scenarios:
- Recommend immediate shutdown (safest but most disruptive)
- Continued operation with increased monitoring (risky but maximizes short-term production)
- Reduced load operation until a scheduled maintenance window (balance of safety and productivity)
-
Orchestrated Response: Based on its analysis, the agent:
- Creates a ticket for a maintenance intervention before the incoming weather front
- Provides the maintenance team with comprehensive diagnostic data and repair instructions
- Places an order for the specific bearing model needed based on inventory checks
- Temporarily reduces the turbine’s load to minimize damage progression
- Suggests adjustments to production forecasts in the energy trading system to account for the reduced capacity
What makes this approach revolutionary is the agent’s ability to dynamically weigh multiple factors and priorities without being constrained to pre-defined decision trees. Traditional systems would require engineers to anticipate and program specific logic paths for countless potential scenarios—essentially trying to codify expert judgment for every possible combination of measurements, weather conditions, and maintenance histories. The agentic approach instead allows the system to reason through the situation using its understanding of wind farm operations, maintenance best practices, and business priorities.
The MCP server provides a standardized interface that dramatically simplifies this integration. Without MCP, connecting an AI agent to these various systems (SCADA, ERP, weather services, maintenance management) would require custom development for each platform—separate authentication methods, data formats, and communication protocols. Each new tool or data source would mean more custom code and maintenance burden. With MCP, the agent can discover available tools, understand their capabilities through semantic descriptions, and interact with them through a consistent interface—reducing development time from months to days and allowing the solution to scale across different sites and equipment types without extensive reconfiguration.
Introducing the Cumulocity MCP Server
We’re excited to share the first release of a Cumulocity MCP Server—an open-source Python implementation that enables LLMs to interact directly with the Cumulocity IoT platform.
Alpha Alert: This is an early version! We’re actively seeking feedback and contributions. Consider this more of a “let’s build something awesome together” than a finished product.
Key capabilities currently include:
- Device Information: List, filter, and retrieve detailed device information
- Measurements: Access time-series data with flexible filtering
- Alarms: Monitor and manage active alarms by severity and status
How to try it out
It does not take more than 10 minutes to try out the Cumulocity MCP Server if you have access to a Cumulocity tenant (just create a free trial tenant if you haven’t) :
-
Install Claude Desktop
-
Configure Claude to use the Cumulocity MCP Server as explained in the GitHub README:
- Prompt your agent:
You're an IoT maintenance specialist. Use the Cumulocity tools to:
1. Provide an overview of the connected devices
2. Check their historical measurements for patterns
3. Identify which devices need immediate attention
5. Recommend specific maintenance actions
- Watch as the agent discovers available devices, analyzes measurements, and provides actionable recommendations—all without writing a single line of code!
- Try out other MCP Servers to add additional tools and capabilities to your agent
The magic here is that MCP enables the LLM to understand the available tools, dynamically explore your device inventory, and make informed decisions about maintenance priorities.
Ready to contribute? Visit github.com/Cumulocity-IoT/cumulocity-mcp-server and join us in building the next generation of intelligent IoT systems.