MCPInstall

open-meteo

by itechmeatBackend & APIs

Integrate Open-Meteo Weather Forecast, Air Quality, and Geocoding APIs: query design, variable selection, timezone/timeformat/units, multi-location batching, and reliable error handling. Use when fetching weather forecasts, air quality/pollen data, or geocoding place names to coordinates via Open-Meteo. Keywords: Open-Meteo, /v1/forecast, /v1/air-quality, geocoding-api, hourly, daily, current, timezone=auto, timeformat=unixtime, models, WMO weathercode, CAMS, GeoNames, httpx, FastAPI, pytest.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/itechmeat/llm-code --skill open-meteo
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads open-meteo automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Open Meteo

Goal

Provide a reliable, production-friendly way to call Open-Meteo APIs (Forecast, Air Quality, Geocoding), choose variables, control time/units/timezone, and parse responses consistently.

Quick navigation

TopicReference
Weather Forecast API referencereferences/forecast-api.md
Air Quality API referencereferences/air-quality-api.md
Geocoding API referencereferences/geocoding-api.md
Provider/model selection (models=)references/models.md
WMO weather interpretation codesreferences/weather-codes.md
Worked examples (geocode → fetch)references/examples.md

Steps

  1. Pick the correct API and base URL

    • Forecast: https://api.open-meteo.com/v1/forecast
    • Air Quality: https://air-quality-api.open-meteo.com/v1/air-quality
    • Geocoding: https://geocoding-api.open-meteo.com/v1/search

  2. Resolve coordinates (if you only have a name)

    • Call Geocoding with name and optional language, countryCode, count.
    • Use the returned latitude, longitude, and timezone for subsequent calls.

  3. Design your time axis (timezone, timeformat, and range)

    • Prefer timezone=auto when results must align to local midnight.
    • If you request daily=..., set timezone (docs: daily requires timezone).
    • Choose timeformat=iso8601 for readability, or timeformat=unixtime for compactness.
      • If using unixtime, remember timestamps are GMT+0 and you must apply utc_offset_seconds for correct local dates.
    • Choose range controls:
      • forecast_days and optional past_days, or
      • explicit start_date/end_date (YYYY-MM-DD), and for sub-daily start_hour/end_hour.

  4. Choose variables minimally (avoid "download everything")

    • Forecast: request only the variables you need via hourly=..., daily=..., current=....
    • Air Quality: request only the variables you need via hourly=..., current=....
    • Keep variable names exact; typos return a JSON error with error: true.

  5. Choose units and model selection deliberately

    • Forecast units:
      • temperature_unit (celsius / fahrenheit)
      • wind_speed_unit (kmh / ms / mph / kn)
      • precipitation_unit (mm / inch)
    • Forecast model selection:
      • default models=auto / “Best match” combines the best models.
      • you can explicitly request models via models=....
      • provider-specific forecast endpoints also exist (provider implied by path). See references/models.md (section "Endpoints vs models=") for examples and doc links.
      • for provider/model-specific selection tradeoffs, see references/models.md.
    • Air Quality domain selection:
      • domains=auto (default) or cams_europe / cams_global.

  6. Implement robust request/response handling

    • Treat HTTP errors and JSON-level errors separately.
    • JSON error format is:
      • {"error": true, "reason": "..."}
    • When requesting multiple locations (comma-separated coordinates), expect the JSON output shape to change to a list of structures.
    • Optionally use format=csv or format=xlsx when you need data export.

  7. Validate correctness with a “known city” check

    • Geocode “Berlin” → Forecast hourly=temperature_2m for 1–2 days → verify timezone and array lengths.
    • Air Quality hourly=pm10,pm2_5,european_aqi → verify units and presence of hourly_units.

Critical prohibitions

  • Do not include out-of-scope APIs in this skill’s implementation guidance: Historical Weather, Ensemble Models, Seasonal Forecast, Climate Change, Marine, Satellite Radiation, Elevation, Flood.
  • Do not omit timezone when requesting daily variables (per docs).
  • Do not assume unixtime timestamps are local time; they are GMT+0 and require utc_offset_seconds adjustment.
  • Do not silently ignore {"error": true} responses; fail fast with the provided reason.
  • Do not request huge variable sets by default; keep queries minimal to reduce payload and avoid accidental overuse.

Definition of done

  • You can geocode a place name and obtain coordinates/timezone.
  • You can fetch Forecast data with at least one hourly, one daily (with timezone), and one current variable.
  • You can fetch Air Quality data for at least one pollutant and one AQI metric.
  • Your client code handles both HTTP-level failures and JSON-level error: true with clear messages.
  • Attribution requirements from the docs are captured for Air Quality (CAMS) and Geocoding (GeoNames).

Release Notes

  • 1.7.0 (2026-10-16) changes the server side, not the request contract of /v1/forecast, /v1/air-quality, and the geocoding API: DWD ICON, ECMWF, KMA, and CMC providers moved to a shared internal structure, the DMI migration finished, and the icon API moved to v1 URLs with an EPS resolution switch (native icon grid domains added). A new environment variable limits the atomic block cache to pressure-level files.
  • Fixes in the same release: HRRR and NAM wind direction is now relative to true north, ECMWF daily maximum/minimum temperatures are correct, missing temperature extrema are derived and daily interval aggregations aligned, and bounding-box requests account for model elevation.

Links

FAQ

What does the open-meteo skill do?

Integrate Open-Meteo Weather Forecast, Air Quality, and Geocoding APIs: query design, variable selection, timezone/timeformat/units, multi-location batching, and reliable error handling. Use when fetching weather forecasts, air quality/pollen data, or geocoding place names to coordinates via Open-Meteo. Keywords: Open-Meteo, /v1/forecast, /v1/air-quality, geocoding-api, hourly, daily, current, timezone=auto, timeformat=unixtime, models, WMO weathercode, CAMS, GeoNames, httpx, FastAPI, pytest.

How do I install open-meteo?

Run npx skills add https://github.com/itechmeat/llm-code --skill open-meteo in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/open-meteo/ (personal) or .claude/skills/open-meteo/ (project).

Which AI agents can use open-meteo?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is open-meteo safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains open-meteo?

It is published by itechmeat in itechmeat/llm-code. It has 96 recorded installs.

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