Aareguru

This MCP server provides live and historical data about the Swiss Aare river, enabling AI assistants to answer questions about swimming conditions, water temperature, flow rates, safety, and forecasts. It exposes tools to fetch current data, historical trends, and safety assessments so you can build proactive, data-driven swimming guidance.

How to use

You connect to the Aareguru MCP Server from an MCP client to query current conditions, temperature, flow, forecasts, and safety assessments. You can access data through available HTTP endpoints when using a cloud deployment, or run the server locally and connect via the client’s local configuration. Use the provided HTTP URL for remote access or the local CLI command to run the server on your machine.

How to install

Prerequisites: a modern Python environment and the FastMCP-compatible tooling (uv). You may also run the server locally using a cloud-like client that executes MCP servers.

Option A — Quick start (cloud, no local install): You connect the server as a custom MCP in your client and point to the remote URL.

Option B — Local installation (recommended for testing): clone the project, install dependencies, and run the server locally.

Configuration and access

HTTP access (remote cloud deployment) can be configured in your MCP client to connect to the remote endpoint.

Local access (stdio) uses a local command to start the MCP server process that your client will communicate with via standard input/output.

{
  "mcpServers": {
    "aareguru": {
      "type": "http",
      "name": "aareguru_mcp",
      "url": "https://aareguru.fastmcp.app/mcp",
      "args": []
    }
  }
}

Local startup and CLI usage

If you prefer running the server locally, you can start it with the CLI entry as shown in the local setup. The example command uses the uv tool to run the MCP server from the aareguru-mcp project directory.

# Start the local MCP server using the standard CLI
uv sync
uv run pytest

Available tools and endpoints

You can query the following tools to obtain data and insights about the Aare river.

Monitoring and observability

The server exposes Prometheus-compatible metrics at /metrics for monitoring tool usage, request counts, durations, and errors. Rate limits protect the endpoints with sensible defaults to prevent abuse.

Security and usage notes

No authentication is required for the cloud quick-start flow when using the public endpoint. Ensure you follow best practices for production deployments, including securing access and monitoring usage.

Troubleshooting

If you encounter connectivity or performance issues, check that the MCP endpoint is reachable, verify rate limit headers in responses, and review the metrics for any abnormal tool usage or errors.

Examples and prompts

Use natural language prompts to retrieve data, such as asking for the current temperature, flow status, or safety level. Combine daily reports with forecasts for proactive decision making.

Project notes

This server provides data sources and tooling to support swimming guidance for the Aare river, with a focus on safety thresholds and seasonal context.

Available tools

get_current_temperature

Fetches the water temperature and returns a temperature value along with Swiss German phrasing to describe the feel of the water.

get_current_conditions

Retrieves full current conditions including temperature, flow, and weather-related context.

get_flow_danger_level

Provides a flow rate assessment with official safety thresholds to indicate swim safety.

list_cities

Lists all monitored cities where data is available.

get_historical_data

Returns historical temperature and flow data for a selected period.

get_forecast

Provides a forecast for temperature and flow for upcoming periods.