aranet4 MCP Server

What does “aranet4” MCP Server do?

The aranet4 MCP Server is a Model Context Protocol (MCP) server designed to manage your Aranet4 CO2 sensor device and its associated local database. By bridging AI assistants and external data sources, this server enables seamless interaction with your device for tasks such as scanning for nearby devices, fetching and storing measurement data, and querying historical sensor readings. It supports automatic updates, assisted configuration, and even visualization of data for clients that support images. The server enhances developer workflows by simplifying the integration of environmental sensor data into broader LLM-powered automations, making it easier to monitor air quality, track historical trends, and manage device settings programmatically.

List of Prompts

No explicit prompt templates are documented in the repository or README.

List of Resources

No explicit resources are documented in the repository or README.

List of Tools

Configuration and Utils:

• init_aranet4_config: Assisted configuration of the Aranet4 device.
• scan_devices: Scans for nearby Bluetooth Aranet4 devices.
• get_configuration_and_db_stats: Retrieves current config.yaml and general statistics from the local SQLite database.
• set_configuration: Sets values within config.yaml.

To update historical data:

• fetch_new_data: Fetches new data from the configured Aranet4 device and saves it to the local database.

To query historical data:

• get_recent_data: Retrieves recent data from the local database; can specify the number of measurements.

Use Cases of this MCP Server

• Environmental Monitoring: Automatically scan and fetch air quality and CO2 data from nearby Aranet4 devices, storing it in a local database for further analysis.
• Historical Data Analysis: Query and review past sensor readings to identify trends or anomalies in air quality over time, benefiting researchers or facility managers.
• Automated Configuration: Use AI-assisted or manual tools to quickly set up and configure Aranet4 devices, reducing setup time for non-technical users.
• Visualization and Reporting: For clients that support image output, generate and visualize plots of recent measurements for easy interpretation and reporting.
• Device Fleet Management: Scan for and manage multiple Aranet4 devices in a space, enabling centralized monitoring and configuration.

How to set it up

Windsurf

1. Prerequisites: Ensure you have Node.js and Python installed.
2. Clone and set up the server:

   git clone git@github.com:diegobit/aranet4-mcp-server.git
   cd aranet4-mcp-server
   

3. Install dependencies: Use uv or pip install . as preferred.
4. Edit Windsurf configuration: Add the aranet4 server to the mcpServers section.
5. Save and restart Windsurf.
6. Verify: Check that your device is detected and data is accessible.

Example JSON:

"mcpServers": {
  "aranet4": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/aranet4-mcp-server/",
      "run",
      "src/server.py"
    ]
  }
}

Note: To secure API keys or sensitive information, use environment variables:

"aranet4": {
  "env": {
    "ARANET4_API_KEY": "your_api_key_here"
  },
  "inputs": {}
}

Claude

1. Follow steps 1–3 above.
2. Edit Claude Desktop config file: Located at ~/Library/Application Support/Claude/claude_desktop_config.json.
3. Add the aranet4 server as shown above.
4. Save and restart Claude Desktop.
5. Use init aranet4 for guided setup.

Cursor

1. Follow steps 1–3 above.
2. Edit Cursor config file: Located at ~/.cursor/mcp.json.
3. Insert the aranet4 server JSON configuration.
4. Save and restart Cursor.
5. Use init aranet4 for guided setup.

Cline

1. Follow steps 1–3 above.
2. Edit your relevant Cline config file.
3. Add the aranet4 server JSON configuration.
4. Save and restart Cline.
5. Use the interactive configuration tool for setup.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "aranet4": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can now use this MCP as a tool with access to all its functions and capabilities. Remember to change “aranet4” to your actual MCP server’s name and replace the URL with your own MCP server URL.

Overview

Our opinion

The aranet4 MCP server provides strong utility for Aranet4 device management and environment sensing, with clear tool exposure and good platform support. However, it lacks documented prompt templates and explicit MCP resource definitions, as well as advanced MCP features like sampling and roots. The setup instructions are practical and detailed, especially for popular AI devtools. Overall, this is a solid and practical MCP implementation for its domain.

MCP Score

Rating: 6/10 – Great device-specific utility, but missing broader MCP features and documentation on prompts/resources.