foehn MCP Server

<h1 align="center"> <img src="https://raw.githubusercontent.com/kayhendriksen/foehn/main/assets/banner.svg" alt="foehn" width="600"> </h1> <p align="center"> <strong>MeteoSwiss Open Data — Python API, CLI & MCP server · tabular as DataFrames/Parquet, gridded as xarray/Zarr</strong> </p> <p align="center"> <a href="https://pypi.org/project/foehn/"> <img src="https://img.shields.io/pypi/v/foehn.svg" alt="PyPI Latest Release"> </a> <a href="https://pypi.org/project/foehn/"> <img src="https://img.shields.io/pypi/pyversions/foehn.svg" alt="Python Versions"> </a> <a href="https://github.com/kayhendriksen/foehn/blob/main/LICENSE"> <img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="MIT License"> </a> <a href="https://scorecard.dev/viewer/?uri=github.com/kayhendriksen/foehn"> <img src="https://api.scorecard.dev/projects/github.com/kayhendriksen/foehn/badge" alt="OpenSSF Scorecard"> </a> <a href="https://pypi.org/project/foehn/"> <img src="https://img.shields.io/pypi/dm/foehn.svg" alt="Monthly Downloads"> </a> </p>

---

foehn downloads every MeteoSwiss OGD collection via the STAC API, converts CSV/TXT station data to Parquet with Polars, and opens gridded collections — NetCDF climate grids, GRIB2 forecasts, and ODIM radar composites — as xarray Datasets or Zarr stores. It can optionally ingest everything into Databricks Unity Catalog Delta tables on a daily schedule, and ships an MCP server so LLMs can query Swiss weather data directly.

<p align="center"> <img src="https://raw.githubusercontent.com/kayhendriksen/foehn/main/assets/mcp_demo.png" alt="Daily weather in Bern, powered by foehn" width="700"> </p> <p align="center"> <em>Daily weather in Bern, powered by foehn's MCP server and MeteoSwiss open data.</em> </p>

Why foehn?

• 20+ collections in one command — weather stations, radar, hail maps, forecasts, climate scenarios, and more
• Tabular and gridded — CSV station data as Polars DataFrames or Parquet; NetCDF, GRIB2 and ODIM radar grids as xarray Datasets or Zarr stores
• MCP server for LLMs — give your favorite LLM live access to MeteoSwiss data with the MCP server
• Significantly smaller on disk — columnar Parquet with Zstandard compression vs. raw CSVs
• Incremental by default — only re-downloads files that changed since your last run, tracked via _last_run.json
• No Spark required locally — download + conversion uses Polars only; Spark is optional for Delta ingestion
• Ships a Declarative Automation Bundle — ready-to-deploy daily job and historical backfill, no pipeline config needed

---

Quick start

pip install foehn
foehn download

Recent data (Jan 1 to yesterday) is downloaded and converted to Parquet under ./data/meteoswiss/.

<img src="https://raw.githubusercontent.com/kayhendriksen/foehn/main/assets/cli_demo.gif" alt="foehn CLI demo" width="800">

---

Installation

From PyPI:

pip install foehn

From source:

git clone https://github.com/kayhendriksen/foehn
cd foehn
pip install -e .

With extras:

pip install "foehn[databricks]"   # PySpark + Delta
pip install "foehn[mcp]"          # MCP server
pip install "foehn[grids]"        # xarray + Zarr for all gridded data (NetCDF, GRIB2, radar)

Requires Python 3.11 or later.

---

Python API

import foehn

df = foehn.load("smn", station="BER", frequency="d")

Load data directly into Polars DataFrames, explore metadata, download to disk, and convert to Parquet — all from Python. See the full Python API documentation.

---

CLI

foehn download smn pollen
foehn load smn --station BER --frequency d

The CLI mirrors the Python API with subcommands for downloading, converting, loading, and inspecting metadata. See the full CLI documentation.

---

Gridded data

ds = foehn.open_dataset("surface_derived_grid", match="rhiresd")  # NetCDF climate grid
ds = foehn.open_dataset("forecast_icon_ch1", match="202605231500-0-t_2m-ctrl")  # one GRIB2 field
ds = foehn.open_dataset("radar_precip", match="cpc2613000000")    # one radar composite
foehn.to_zarr("surface_derived_grid", match="rhiresd")            # Zarr store

NetCDF climate grids/normals/scenarios, GRIB2 forecasts (ICON-CH1/CH2, KENDA), and HDF5/ODIM radar composites all open as xarray Datasets instead of DataFrames. One extra covers them: pip install "foehn[grids]". See the gridded data documentation.

---

MCP server

{
  "mcpServers": {
    "foehn": {
      "command": "foehn",
      "args": ["mcp"]
    }
  }
}

Give any MCP-compatible LLM live access to MeteoSwiss data. See the full MCP server documentation.

---

Documentation

Collections	All 20+ MeteoSwiss datasets, categories, and time slice conventions
Python API	Loading data, metadata, downloading, and Parquet conversion
Gridded data	NetCDF grids as xarray Datasets and Zarr stores
CLI	All subcommands, flags, and environment variables
MCP Server	Setup, configuration, and available tools
Databricks Pipeline	Declarative Automation Bundle deployment

---

Data sources

STAC API	https://data.geo.admin.ch/api/stac/v1
Documentation	https://opendatadocs.meteoswiss.ch
MeteoSwiss OGD	https://github.com/MeteoSwiss/opendata

---

License

MIT

<!-- mcp-name: io.github.kayhendriksen/foehn -->