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Weather for Grown Ups

Official registry Maps, travel and weather

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

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

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

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  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.

Frequently asked questions

What is Weather for Grown Ups?

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

How do I install Weather for Grown Ups?

It runs on your machine. In Claude Code: claude mcp add weather-for-grown-ups -- npx -y weather-for-grown-ups@0.5.5 mcp. For other clients, paste the JSON configuration from this page into their MCP settings.

Does Weather for Grown Ups need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is Weather for Grown Ups open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains Weather for Grown Ups?

It is published by janhelcl. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.