How I Used the Model Context Protocol (MCP) to Coordinate My DIY Smart Home

In recent months, I embarked on a personal project to upgrade my home with a smart automation system. The challenge was to integrate multiple components—motion sensors, temperature sensors, lights, and an IP camera—into a cohesive and context-aware environment.

Initially, each component was working in isolation. A motion sensor could turn on a light, a camera could start recording when triggered, and the temperature sensor could send its data periodically. But none of these devices had any understanding of the overall context. This limitation became apparent as the system grew more complex.

This article explores how I introduced a custom implementation of a Model Context Protocol (MCP) to unify the behavior of all these components and create a more intelligent and coordinated smart home system.

System Overview

The hardware and software stack included:

A mini PC running Ubuntu acting as the central server
ESP32 boards equipped with motion and temperature sensors, communicating via MQTT
A Raspberry Pi connected to an IP camera
Smart lights controlled via RESTful API
A Python-based automation controller
A WebSocket server for context synchronization

Initially, each component operated using simple event-action scripts. For example, when the motion sensor detected movement, a script would trigger the lights to turn on. However, the logic quickly became fragile and difficult to manage as additional devices were added.

The Core Issue: Lack of Shared Context

The primary issue was the absence of a shared understanding of the environment. Consider the following scenarios:

The lights turned on even when I was not home.
The IP camera kept recording even while I was in the living room.
Motion detection events were triggering the same reactions repeatedly, regardless of other conditions.
Environmental conditions such as brightness or time of day were not considered.

Each of these problems stemmed from the lack of a global state that all components could refer to. Each device operated based on its own inputs, without knowledge of what others were sensing or deciding.

Introducing the Model Context Protocol (MCP)

To solve this, I implemented a lightweight version of the Model Context Protocol. The goal was to establish a centralized context server that all components could publish to and subscribe from.

Key Principles

Each agent (sensor, light, camera, etc.) publishes its local state to a central context server.
The server maintains a global context model in memory, updated incrementally as new events arrive.
When significant changes occur in the context, interested agents are notified.
Agents react locally, using the latest context to decide whether or not to act.

This approach decouples the behavior logic from hardcoded trigger rules and instead allows decisions to be made based on consistent, shared context.

Technical Implementation

The Context Server was implemented in Python using the asyncio and websockets libraries. MQTT was used for communication with ESP32 devices, and TinyDB was used to persist the context state for inspection and recovery.

Each component sends updates in JSON format. For example:

{
  "source": "motion_sensor_living_room",
  "motion": true,
  "timestamp": "2025-07-06T14:21:45Z"
}

The context server processes this update and modifies the global context accordingly. A simplified context might look like this:

{
  "presence": true,
  "motion": true,
  "is_dark": false,
  "temperature": 27.8,
  "last_motion": "2025-07-06T14:21:45Z"
}

The context server then notifies all subscribers (e.g., the light controller, the camera module) about the updated context.

Real-World Use Case: Automated Presence Management

A particularly useful application of MCP was for presence detection. Here's how the system behaves with MCP:

When motion is detected while the context indicates that the house is "empty", the context updates presence to true.
The camera module, upon detecting presence: true, automatically stops recording to respect privacy.
The lighting controller turns on lights only if presence: true and is_dark: true.
Context updates are logged and stored for historical analysis or future machine learning applications.

Benefits of Using MCP

Implementing MCP brought several advantages:

Improved consistency: All agents operate based on a unified context model.
Reduced false triggers: Actions are taken only when multiple conditions are satisfied.
Easier debugging: Centralized logging of context changes makes it easier to trace issues.
Extensibility: New agents or logic can be added without modifying existing ones, as long as they follow the context protocol.

Security and Reliability Considerations

While this system is not exposed to the public internet, I added basic measures:

Token-based authentication for WebSocket clients
Input validation using jsonschema
Context versioning and timestamp tracking
Manual override controls via command line interface

Future improvements may include:

TLS encryption for WebSocket communication
Integration with a dashboard for visual monitoring
Rule-based automation engine using YAML or JSON configuration

Conclusion

Building a smart home system that behaves intelligently requires more than just connecting devices. The key lies in providing a shared understanding of context that all devices can refer to.

By implementing a simple Model Context Protocol, I was able to transform a fragile collection of scripts into a robust and extensible system where each component plays its part based on the global state.

This approach is scalable, reusable, and applicable well beyond home automation—any distributed system can benefit from having a centralized or decentralized context synchronization strategy.

If you're interested in the source code for the context server or the agents, feel free to reach out. I’d be happy to publish it in a follow-up article.

astro @clastrox