Aranet4 MCP Server

The Aranet4 MCP server bridges the gap between a physical CO₂ sensor and AI assistants, allowing developers to treat real‑world environmental data as first‑class resources within an MCP workflow. By exposing the sensor’s BLE interface through a lightweight Python service, the server turns raw measurements into structured records stored in an SQLite database. This persistence layer makes historical analysis, trend spotting, and anomaly detection possible without the need for custom data pipelines.

At its core, the server offers a set of intuitive tools that automate common tasks: scanning for nearby Aranet4 devices, fetching the latest samples from the device’s memory, and updating a local database. Once data is stored, AI clients can query recent readings or retrieve measurements for any specified time range. For visual insight, the server can generate plots that are returned as image attachments—an invaluable feature for clients that support rich media, enabling quick interpretation of CO₂ levels over time.

Configuration is designed to be developer‑friendly. After installation, a simple command walks the user through pairing and setting the device’s MAC address, eliminating manual edits to configuration files. The server also exposes tools for inspecting and modifying its own , ensuring that adjustments can be made on the fly without restarting services. Automated data collection is supported via a cronjob or macOS LaunchAgent, guaranteeing that the local database remains up‑to‑date without manual intervention.

Developers can leverage this MCP server in a variety of scenarios: building smart‑building dashboards that automatically surface indoor air quality metrics, creating compliance tools that log CO₂ levels for regulatory reporting, or integrating environmental context into conversational agents that adjust responses based on real‑time air quality. Because the server abstracts BLE communication and data persistence, teams can focus on higher‑level logic—such as predictive maintenance or occupant comfort algorithms—while relying on a robust, AI‑ready data source.