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Weather for Grown Ups

Registre officiel Cartes, voyage et météo

Operational NWP, ensembles, AI weather models, regional forecasts, history, and verification.

Code source Documentation

Installation

Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).

NPM package weather-for-grown-ups
Claude Code 
claude mcp add weather-for-grown-ups -- npx -y weather-for-grown-ups@0.5.5 mcp
Configuration JSON (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "weather-for-grown-ups": {
            "command": "npx",
            "args": [
                "-y",
                "weather-for-grown-ups@0.5.5",
                "mcp"
            ]
        }
    }
}

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Sommaire
  1. Weather for Grown Ups
  2. 30-second start
  3. The contract
  4. Ask harder questions
  5. Models
  6. Agents
  7. Go deeper
  8. License

Weather for Grown Ups

Weather is the hello-world of agent tools. WFG is for when “temperature tomorrow” stops being enough.

One query language for operational NWP: global and regional models, deterministic and ensemble forecasts, physics and AI, current runs and history.

Ask the same atmospheric question across models without flattening what makes the models different.

30-second start

Requires Node.js 20+.

npx weather-for-grown-ups catalog --dataset all --search wind --json

npx weather-for-grown-ups query \
  --dataset gfs \
  --lat 50.08 --lon 14.43 \
  --at 2026-08-24T12:00:00Z \
  --vars temperature,wind \
  --levels 850,700,500 \
  --json

For repeated use:

npm install -g weather-for-grown-ups
wfg --help

The contract

Normal atmospheric access is four orthogonal choices:

dataset × geometry × time × selection
{
  "dataset": "gefs",
  "geometry": {
    "type": "point",
    "latitude": 50.08,
    "longitude": 14.43
  },
  "time": {
    "at": "2026-08-30T12:00:00Z"
  },
  "selection": {
    "variables": ["temperature", "wind"],
    "pressureLevelsHpa": [850, 700, 500]
  },
  "ensemble": {
    "quantiles": [0.1, 0.5, 0.9]
  }
}

Change the dataset; keep the question. Unsupported combinations fail rather than being coerced into fake symmetry.

Read the unified atmospheric API →

Ask harder questions

Start with the question. Let the agent build the forecast investigation from scratch.

01 — Give me the serious forecast

What does the atmosphere over Prague look like tomorrow afternoon? Cover the surface and vertical structure, compare the main global guidance, and show which parts are robust across the ensembles.

GFS + IFS → pressure profiles → GEFS + IFS ENS → regional model if useful

02 — Is a front coming?

Is a frontal passage expected near Prague tomorrow? If so, when does it arrive, what changes through the column, and how consistent are the models?

GFS + IFS → time evolution → pressure profiles → GEFS + IFS ENS → align latest vs previous runs

03 — AI vs physics

How do AIFS and IFS describe the atmosphere over Prague tomorrow? Where do they disagree through time and height, and is that disagreement large relative to their ensemble uncertainty?

align IFS, AIFS → pressure profiles → align IFS ENS, AIFS ENS

04 — Plan a paragliding day

I’m planning to paraglide around Bassano tomorrow. How does the convective window develop through the day, what do the wind profile and stability look like, and how robust is the picture across ensembles and global/regional guidance?

profiles → parcel diagnostics → wind through the column → time evolution → ensembles → global + regional models

05 — Verify an old forecast

What did GFS predict three days ago for Prague today, and how well did that forecast verify?

archived forecast → analysis / radiosonde → error → lead provenance

Explore the investigation gallery →

Models

GFS · GEFS · AIGFS · AIGEFS · HGEFS · IFS · IFS ENS · AIFS · AIFS ENS · ICON-D2 · ICON-D2-EPS · AROME · PE-AROME · GFS analysis · GEFSv12 reforecast

Capabilities come from the catalog. Native grids, cadence, domains, members, fields and provenance remain explicit.

wfg catalog --dataset all --json

Agents

WFG exposes the same core through CLI and MCP.

Shell available → CLI.
No shell / remote → MCP.

npx weather-for-grown-ups mcp

Add the portable WFG skill, then pick a setup guide: Codex · Claude Code · Hermes · OpenClaw · other agents

Go deeper

Examples · Installation · API contract · Catalog · History · Architecture · All docs

License

MIT. See LICENSE.

Questions fréquentes

Qu'est-ce que Weather for Grown Ups ?

Operational NWP, ensembles, AI weather models, regional forecasts, history, and verification.

Comment installer Weather for Grown Ups ?

Il s'exécute sur votre machine. Dans Claude Code : claude mcp add weather-for-grown-ups -- npx -y weather-for-grown-ups@0.5.5 mcp. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.

Faut-il une clé API pour Weather for Grown Ups ?

Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.

Weather for Grown Ups est-il open source ?

Oui. Son code est sur GitHub sous licence MIT.

Qui maintient Weather for Grown Ups ?

Il est publié par janhelcl. AI.tn le référence sans le maintenir ni le recommander.

Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.