Manage Aranet4 CO2 Sensor Data
Manage Aranet4 CO2 sensors with this MCP server. Scan devices, fetch and store data locally, and query historical measurements with visualizations.
Why it matters
Connect to your Aranet4 CO2 sensor via Bluetooth, retrieve data, and store it in a local SQLite database for historical tracking and analysis.
Outcomes
What it gets done
Scan for nearby Aranet4 devices.
Fetch new CO2 measurements and save them to a local database.
Query historical data by date range or recency.
Assist with device setup and configuration.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-aranet4 | bash Capabilities
Tools your agent gets
Assistance with Aranet4 device setup and configuration
Scan for nearby Aranet4 Bluetooth devices
Retrieve current config and overall statistics from local SQLite database
Set values in config.yaml configuration file
Retrieve new data from configured Aranet4 device and save to local database
Get recent measurements from local database with configurable count
Get data for a specific time period from local database
Overview
Aranet4 MCP Server
What it does
The Aranet4 MCP Server connects your Aranet4 CO2 sensor to AI clients. It enables scanning for devices, fetching data from the sensor's memory, and storing it locally in an SQLite database. You can query historical measurements and request data visualizations.
How it connects
Use this tool when you need to integrate Aranet4 CO2 sensor data into an AI application for monitoring, analysis, or automation. It's suitable for tracking CO2 levels over time and accessing past readings through an AI interface.
Source README
aranet4-mcp-server
MCP server to manage your Aranet4 CO2 sensor. Built upon Aranet4-Python.
Features:
- Scan for nearby devices.
- Fetch new data from embedded device memory and save to a local sqlite db for tracking and later viewing. For automatic updates, see at the bottom.
- Ask questions about recent measurements or about a specific past date.
- [For MCP clients that support images] Ask data to be plotted to also have a nice visualization!
- Assisted configuration! 💫 After installation, just ask
init aranet4in your client to set up everything for the mcp server to work with your device.
Requirements
An Aranet4 device already paired. The device uses BLE, so it won't be shown in regular bluetooth search. On MacOS, one way is to install LightBlue from the App Store.
Installation
Clone repo:
git clone git@github.com:diegobit/aranet4-mcp-server.git` cd aranet4-mcp-serverPrepare environment:
- Recommended (with uv): Nothing to do. The provided
pyproject.tomlhandles dependencied and virtual environments. - Alternative (with pip): install with
pip install .
- Recommended (with uv): Nothing to do. The provided
Add to MCP client configuration:
"aranet4": { "command": "{{PATH_TO_UV}}", // run `which uv` "args": [ "--directory", "{{PATH_TO_SRC}}/aranet4-mcp-server/", "run", "src/server.py" ] }- Claude Desktop MacOS config file path:
~/Library/Application Support/Claude/claude_desktop_config.json - Cursor MacOS config file path:
~/.cursor/mcp.json
- Claude Desktop MacOS config file path:
Configure:
- Recommended (AI assisted config!): start your client and ask
init aranet4to get a guided configuration. - Alternative (manual): edit file
config.yaml. You need to provide the mac address and the device name. You can get the mac address witharanetctl --scanfrom Aranet4-Python (installed with this repo dependencies).
- Recommended (AI assisted config!): start your client and ask
Dockerfile
Dockerfile is available. Remember to pass env variables or update config.yaml.
List of tools
Configuration and utils:
init_aranet4_config: assisted configuration of device.scan_devices: scan nearby bluetooth aranet4 devices.get_configuration_and_db_stats: get current config.yaml and general stats from the local sqlite3 db.set_configuration: set values in config.yaml.
To update historical data:
fetch_new_data: fetch new data from configured nearby aranet4 device and save to local db.
To query historical data:
get_recent_data: get recent data from local db. Can specify how many measurements.get_data_by_timerange: get data in specific timerange from local db. Can specify how many measurements (careful, if the range is big and the limit is low, datapoints will be skipped).For both, ask to receive a plot to have it generated and displayed.
Automatic data fetch job
If you want your local db to always be updated, you can setup a cronjob or a launch agent that fetches data automatically every few hours. In MacOS, do as follows:
- Configure absolute paths in
com.diegobit.aranet4-fetch.plist. - Install LaunchAgent:
cp com.diegobit.aranet4-fetch.plist ~/Library/LaunchAgents/ launchctl load ~/Library/LaunchAgents/com.diegobit.aranet4-fetch.plist
For other platforms, just run fetch-job.py periodically however you prefer.
Discussion
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