IoT Device Control MCP Server

The Iot Mcp Server bridges the gap between AI assistants and the physical world by offering a unified, protocol‑agnostic interface for interacting with Internet of Things devices. Traditional IoT integrations often rely on vendor‑specific SDKs or ad hoc REST endpoints, making it difficult for AI models to discover and control devices in a consistent manner. By exposing device commands, state queries, and real‑time updates through the Model Context Protocol (MCP), this server allows Claude or other AI assistants to treat IoT devices as first‑class resources—just like files, databases, or web services. This abstraction dramatically reduces the friction for developers who need to embed smart‑home automation, industrial monitoring, or remote device management into conversational agents.

At its core, the server supports three principal actions: send_command, get_device_state, and subscribe_to_updates. These tools translate high‑level intent from the AI into MQTT messages that are routed to the appropriate device topics. The tool enables the assistant to issue operations such as turning lights on/off, adjusting thermostats, or triggering alarms. fetches the latest telemetry—temperature readings, battery levels, or occupancy status—allowing the assistant to provide contextually relevant responses. establishes a streaming subscription so that the AI can react instantly to sensor changes, enabling real‑time monitoring dashboards or proactive alerts.

The server’s design emphasizes flexibility and scalability. It supports both Server‑Sent Events (SSE) and standard I/O transports, making it easy to deploy in cloud environments or embedded systems. By configuring the and , developers can integrate with any existing MQTT infrastructure, whether it’s a local Mosquitto broker or a managed cloud service. The clear separation between the IoT and Memory MCP servers also means that developers can choose to deploy only the component they need, keeping resource usage minimal.

Real‑world scenarios illustrate its value: a home automation assistant can automatically dim lights when motion is detected, an industrial AI monitor can trigger maintenance workflows based on vibration thresholds, and a smart building system can balance HVAC loads across zones using conversational commands. Because the server presents these capabilities as MCP tools, developers can compose complex workflows by chaining tool calls—e.g., “if the temperature exceeds 30 °C, turn on the fan and notify the building manager”—without writing custom integration code.

In summary, the Iot Mcp Server turns heterogeneous IoT ecosystems into a single, discoverable resource pool for AI assistants. Its MQTT‑backed command and state model, coupled with real‑time subscriptions, empower developers to build responsive, contextually aware applications that seamlessly blend digital conversation with physical action.