# Weather for Grown Ups (npm · weather-for-grown-ups)

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

- Trust score: 81/100 (high trust)
- Change this week: +5
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-21

## Components

- npm · `weather-for-grown-ups`: 81/100 (this document), [markdown](https://verifymcp.io/servers/janhelcl-weather-for-grown-ups/weather-for-grown-ups.md), [page](https://verifymcp.io/servers/janhelcl-weather-for-grown-ups/weather-for-grown-ups)

## Channel facts

- Registry: `npm`
- Package: `weather-for-grown-ups`
- Version: `0.5.5`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-21.

- **Supply Chain Security**: 98/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - No install/post-install scripts declared.
  - 15 of 45 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 97/100
  - Source repository is publicly reachable at the declared URL.
  - Cryptographically verified build provenance (signed, bound to janhelcl/weather-for-grown-ups).
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 11 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 61/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 1607 tokens (~229/item across 7 items; 7 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 40/100
  - Stability observed for 12 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 75/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 14% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 7 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 8 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### How do I install the Weather for Grown Ups MCP server?

Weather for Grown Ups runs locally as an npm package, launched with npx -y weather-for-grown-ups. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add janhelcl-weather-for-grown-ups -- npx -y weather-for-grown-ups
```

### Cursor

```json
{
  "mcpServers": {
    "janhelcl-weather-for-grown-ups": {
      "command": "npx",
      "args": [
        "-y",
        "weather-for-grown-ups"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "janhelcl-weather-for-grown-ups": {
      "command": "npx",
      "args": [
        "-y",
        "weather-for-grown-ups"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add janhelcl-weather-for-grown-ups -- npx -y weather-for-grown-ups
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "janhelcl-weather-for-grown-ups": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "weather-for-grown-ups"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add janhelcl-weather-for-grown-ups --command npx --arg -y --arg weather-for-grown-ups
```

### Hermes

```yaml
mcp_servers:
  janhelcl-weather-for-grown-ups:
    command: "npx"
    args: ["-y", "weather-for-grown-ups"]
```

### Netclaw

```json
{
  "McpServers": {
    "janhelcl-weather-for-grown-ups": {
      "Transport": "stdio",
      "Command": "npx",
      "Arguments": [
        "-y",
        "weather-for-grown-ups"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add janhelcl-weather-for-grown-ups -t stdio -c npx -a -y weather-for-grown-ups
```

### Other

```json
{
  "mcpServers": {
    "janhelcl-weather-for-grown-ups": {
      "command": "npx",
      "args": [
        "-y",
        "weather-for-grown-ups"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-20 (score 81, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-17 (score 80, +4)

- [functional improvement] Stability: unverified → 0.27

### 2026-09-09 (score 76)

First indexed and scored.

## MCP tools (7)

### `search_catalog` (~220 tokens)

Search atmospheric datasets and capabilities

Search one canonical catalog across all atmospheric datasets. Results use shared variable, field and diagnostic IDs and explicitly list which datasets support each match. Use spatialScope and coverage to discover global or limited-area datasets that fully cover a point or bounded area; capability rows expose native grid, nominal resolution, cadence and horizon. For GEFS, forecastKind can distinguish operational capabilities from the narrower GEFSv12 reforecast subset, so retrospective queries are discoverable without pretending operational-only diagnostics exist.

Input parameters:

- `classification` (string)
- `coverage`: Return only datasets whose declared spatial domain fully covers this point or bounded area.
- `datasets` (array)
- `forecastKind` (string): Forecast population filter. Currently supported only with datasets=[gefs]; reforecast selects the GEFSv12 retrospective capability subset.
- `limit` (integer)
- `search` (string)
- `sections` (array)
- `spatialScope` (string): Dataset spatial-domain filter. limited_area selects regional/convection-permitting datasets.
- `temporalSemantics` (string)

Output parameters:

- `datasetCapabilities` (array)
- `matches` (array)
- `query` (object)
- `totalMatches` (integer)
- `truncated` (boolean)

### `query_atmosphere` (~225 tokens)

Query atmospheric state

Query atmospheric state through one dataset × geometry × time × selection contract across every public dataset: gfs, aigfs, aigefs, hgefs, icon-d2, icon-d2-eps, arome, pe-arome, gefs, ifs, aifs, aifs-ens, ifs-ens, gfs-analysis. Selection may be omitted for the dataset-aware default. Dataset-native domain, cadence, grid, provenance, deterministic/member-first semantics and capability limits stay explicit; unsupported combinations fail rather than being coerced into fake symmetry.

Input parameters:

- `aggregate` (object)
- `dataset` (string, required)
- `ensemble` (object)
- `forecast` (object)
- `geometry` (required)
- `limits` (object)
- `selection` (object)
- `source` (string): GFS-only source override. Omit for automatic routing: AWS S3 for point/multi-point/time-series/transect access, NOMADS for area subsets, and the resolution-matched archive for explicit old runs.
- `time` (required)

Output parameters:

- `dataset` (string)
- `geometryType` (string)
- `internalDatasetId` (string)
- `kind` (string)
- `result`
- `role` (string)
- `timeType` (string)

### `diagnose_atmosphere` (~231 tokens)

Derive atmospheric diagnostics

Run shared layer, profile or parcel physics through the same dataset and point/time vocabulary. Deterministic datasets evaluate the shared kernels once; ensemble datasets evaluate nonlinear diagnostics member by member before aggregation. Dataset-native cadence and capability limits remain explicit; AIGFS, AIGEFS and HGEFS do not expose parcel diagnostics because the AI constituent surface inventory lacks the required parcel initialization state, while AIFS and AIFS ENS currently expose layer/profile diagnostics but keep parcel diagnostics as an explicit capability boundary; AIGEFS, HGEFS and AIFS ENS evaluate supported diagnostics member by member before aggregation; HGEFS additionally preserves GEFS-versus-AIGEFS constituent identity.

Input parameters:

- `dataset` (string, required)
- `diagnostic` (required)
- `ensemble` (object)
- `forecast` (object)
- `geometry` (object, required)
- `source` (string): GFS-only source override. Omit for automatic routing: AWS S3 for point/multi-point/time-series/transect access, NOMADS for area subsets, and the resolution-matched archive for explicit old runs.
- `time` (required)

Output parameters:

- `dataset` (string)
- `geometryType` (string)
- `internalDatasetId` (string)
- `kind` (string)
- `result`
- `role` (string)
- `timeType` (string)

### `compare_runs` (~191 tokens)

Compare forecast runs

Compare forecast initialization cycles for GFS, GEFS, deterministic ECMWF IFS, or ECMWF IFS ENS through one dataset-aware contract. Deterministic GFS and IFS return newer-minus-older changes; GEFS and IFS ENS return shifts between independently summarized ensemble distributions and never treat member labels as trajectories. IFS ENS can compare 6-hourly cycles or use a 12-hour stride for long-range 00/12Z ensemble comparisons.

Input parameters:

- `anchorRun`: Forecast initialization: latest, latest_complete where supported, or an explicit timezone-aware ISO cycle
- `cycleStrideHours`
- `cycles` (integer)
- `dataset` (string, required)
- `ensemble` (object)
- `geometry` (object, required)
- `gfsGrid` (string)
- `selection` (object, required)
- `thresholdGte` (number)
- `time` (object, required)

Output parameters:

- `datasets` (array)
- `operation` (string)
- `result`

### `compare_datasets` (~236 tokens)

Compare atmospheric datasets

Compare only explicitly registered, scientifically compatible atmospheric dataset pairs at one point and valid time. Registered pairs: gfs↔gefs, gfs↔ifs, gefs↔ifs-ens, ifs↔ifs-ens, gfs↔aigfs, ifs↔aifs, aigfs↔aifs, gefs↔aigefs, ifs-ens↔aifs-ens, hgefs↔gefs, hgefs↔aigefs, ifs↔icon-d2, ifs↔arome, gfs↔icon-d2, ifs-ens↔icon-d2-eps, ifs-ens↔pe-arome. Pair contracts choose pressure-level or field selection explicitly. Global↔regional strategies require one shared explicit initialization cycle, sample each native grid independently at the requested coordinate, and never silently regrid or downscale. Ensemble comparisons preserve native populations and never pair member labels as trajectories. Differences, spread shifts, and raw member fractions are descriptive model evidence, not forecast error or calibrated uncertainty.

Output parameters:

- `datasets` (array)
- `operation` (string)
- `result`

### `verify_forecast` (~155 tokens)

Verify an archived forecast

Verify archived GFS forecasts against either later GFS analysis or IGRA v2.2 radiosondes. Atomic form uses time.at plus one leadHours value. Both references support a bounded skill-summary form using time.from/time.to plus up to three leadHours values: WFG deterministically samples at most eight nominal verification times and aggregates count, signed bias, MAE and RMSE by lead × pressure × field while retaining failed evaluations explicitly. GFS-analysis summaries are analysis-minus-forecast on the native 0.5° Grid 4 archive; IGRA summaries are observation-minus-forecast and may select an explicit or nearby station. IGRA remains a verification reference here, not a gridded query_atmosphere dataset.

Output parameters:

- `datasets` (array)
- `operation` (string)
- `result`

### `find_analogs` (~123 tokens)

Find historical atmospheric analogs

Find locally materialized historical analyses similar to one target atmospheric profile. The current dataset is gfs-analysis; similarity uses the existing standardized profile metric and U/V wind representation. This is model-state similarity, not climatological rarity or impact-specific similarity.

Input parameters:

- `count` (integer)
- `dataset` (string)
- `excludeWithinHours` (integer)
- `fetchTargetIfMissing` (boolean)
- `geometry` (object, required)
- `pressureLevelsHpa` (array, required)
- `time` (object, required)
- `variables` (array, required)

Output parameters:

- `datasets` (array)
- `operation` (string)
- `result`

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/janhelcl-weather-for-grown-ups/weather-for-grown-ups#diagnostics

## Score history

- 2026-09-21: 81
- 2026-09-20: 81
- 2026-09-19: 80
- 2026-09-18: 80
- 2026-09-17: 80
- 2026-09-16: 76
- 2026-09-15: 76
- 2026-09-14: 76
- 2026-09-13: 76
- 2026-09-12: 76
- 2026-09-11: 76
- 2026-09-10: 76
- 2026-09-09: 76

## Common questions

### What is the Weather for Grown Ups MCP server?

Weather for Grown Ups is an MCP server listed in the public MCP registry as io.github.janhelcl/weather-for-grown-ups. Operational NWP, ensembles, AI weather models, regional forecasts, history, and verification. This page covers its npm package (weather-for-grown-ups).

### Is the Weather for Grown Ups MCP server safe to use?

Weather for Grown Ups scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. It declares no install or post-install scripts. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Weather for Grown Ups MCP server expose?

Weather for Grown Ups exposes 7 tools: search_catalog, query_atmosphere, diagnose_atmosphere, compare_runs, compare_datasets, and 2 more. Their descriptions and schemas cost roughly 1,381 tokens of context every time the server is loaded.

### Is the Weather for Grown Ups MCP server still maintained?

Weather for Grown Ups is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

### What licence is the Weather for Grown Ups MCP server under?

Weather for Grown Ups declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- npm package: https://www.npmjs.com/package/weather-for-grown-ups
- Socket report: https://socket.dev/npm/package/weather-for-grown-ups
- Repository: https://github.com/janhelcl/weather-for-grown-ups
- Changelog RSS feed: https://verifymcp.io/servers/janhelcl-weather-for-grown-ups/weather-for-grown-ups.xml
- Changelog JSON feed: https://verifymcp.io/servers/janhelcl-weather-for-grown-ups/weather-for-grown-ups.json
- HTML version of this page: https://verifymcp.io/servers/janhelcl-weather-for-grown-ups/weather-for-grown-ups