About Weather for Grown Ups
Weather for Grown Ups provides a unified way to query operational numerical weather prediction datasets, from global and regional models to ensembles and AI forecasts. It is for agents and users who need to compare forecast runs, investigate atmospheric conditions, or verify past forecasts while preserving model-specific details.
Key features
- 01Query global and regional forecast datasets
- 02Compare deterministic and ensemble forecasts
- 03Access physics-based and AI models
- 04Inspect archived forecasts and verification
- 05Retain model grids, cadence, and provenance
Use cases
- 01Compare model guidance for a location
- 02Assess ensemble agreement on a forecast
- 03Examine atmospheric profiles over time
- 04Check an older forecast against observations
MCP support
The MCP server exposes the same core weather-query capabilities available through the CLI to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
7Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| limit | integer | No | |
| search | string | No | |
| coverage | any | No | Return only datasets whose declared spatial domain fully covers this point or bounded area. |
| datasets | array | No | |
| sections | array | No | |
| forecastKind | enum | No | Forecast population filter. Currently supported only with datasets=[gefs]; reforecast selects the GEFSv12 retrospective capability subset.operational | reforecast |
| spatialScope | enum | No | Dataset spatial-domain filter. limited_area selects regional/convection-permitting datasets.global | limited_area |
| classification | enum | No | raw | derived |
| temporalSemantics | enum | No | instantaneous | accumulation | average | maximum |
Details
From the project READMEWeather 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.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.