Servidor MCP de Automação Residencial

Overview

The Servidor MCP de Automação Residencial is a lightweight Model Context Protocol (MCP) server designed to bridge AI assistants with home‑automation devices. By exposing a set of high‑level commands—such as turning lights on or off, adjusting thermostat settings, locking or unlocking doors, and retrieving a general status report—the server allows an AI assistant to control a smart home through natural language queries. This solves the common pain point of integrating disparate IoT devices into a single, conversational interface: developers no longer need to write custom adapters for each vendor; instead they can rely on the MCP abstraction.

At its core, the server listens for MCP messages and delegates them to a command processor. The processor interprets user intents and routes them to specific action modules located under . Each module encapsulates the logic for a particular domain (lights, doors, temperature), keeping the codebase modular and extensible. When an AI assistant sends a request like “Set the living room lights to 50%,” the server translates that into an internal command, executes the corresponding Python function, and returns a structured response. This design keeps the AI client agnostic of the underlying hardware details while still providing fine‑grained control.

Key capabilities include:

• Unified command interface: A single set of MCP endpoints for all supported devices, simplifying client development.
• Modular action architecture: Easy addition of new device types by creating a new module in .
• Terminal‑based control: Built‑in CLI commands for quick manual testing and debugging.
• Status aggregation: A “general status” command that collates the state of lights, doors, and temperature into one concise report.

Typical use cases span from personal home automation to educational projects. A homeowner can ask their AI assistant to “Dim the kitchen lights” or “Close all doors,” and the server will translate those requests into concrete actions on connected devices. In a teaching environment, students can experiment with MCP concepts by extending the server to support new sensors or actuators without altering the AI side of the workflow. Moreover, because MCP is language‑agnostic, the same server can be paired with different AI platforms (Claude, GPT‑4, etc.) simply by configuring the client endpoint.

Integration into existing AI pipelines is straightforward: the AI assistant sends a structured MCP request (JSON payload) to the server’s endpoint, receives a response, and can incorporate that information into its next reply. The server thus acts as an intermediary layer that abstracts hardware complexity, enabling developers to focus on building richer conversational experiences rather than low‑level device control.