foehn
MCP server for querying MeteoSwiss open weather data, providing access to station data, forecasts, radar composites, and more via natural language.
README
<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 -->
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