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Smarter Weather

by io.github.smarterweather

Get forecast distribution

get_forecast_distribution
Read-onlyIdempotent

Probabilistic forecast guidance from NBM for one aspect of the weather: percentile ranges (p10-p90), exceedance probabilities, and ensemble spread. Use this for any question about odds, ranges, potential or confidence ("how much could we get", "worst case for the wind", "how sure is this") -- a deterministic forecast value cannot answer one. Reading the percentiles: p50 is the most likely outcome, p90 is the reasonable worst case when the risk is the high end (snow totals, wind, rainfall), and p10 is the reasonable worst case when the risk is the low end (cold, minimum visibility, ceiling). A single percentile is not the forecast -- report the likely value with the tail that matters, and label which is which. Aspects: precip (PoP, QPF + percentiles), snow (accumulation percentiles, >1/2/4in probabilities, snow level), ice (freezing rain, accretion), temperature (temp/dewpoint + stddev), wind (speed/gust percentiles), severe (hail/tornado/damaging-wind probabilities), aviation (LIFR/IFR/MVFR visibility + ceiling probabilities), confidence (ensemble stddev; low spread = settled forecast, high spread = details still in play). Examples: {"location": "Denver", "aspect": "snow", "hours": 72} or {"lat": 32.9, "lon": -97.0, "aspect": "severe"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude in decimal degrees (-90 to 90). Most tools also accept a `location` place-name string instead of lat/lon.
lonNoLongitude in decimal degrees (-180 to 180). For continental US use negative values (west of the prime meridian).
hoursNoForecast hours (1-264). Default varies by aspect (48-72).
aspectYesWhich distribution family to return (see tool description).
locationNoFree-text place: city ("Denver"), city+state ("Portland, OR"), US ZIP ("50219"), or "lat,lon" ("39.74,-104.99"). Provide either this OR explicit lat+lon, not both.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursYes
aspectYes
seriesYes
locationYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds substantial behavioral context beyond annotations: how to interpret p50/p10/p90, that a single percentile is not the forecast, and how ensemble spread maps to forecast confidence. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but densely packed with necessary information: use cases, percentile interpretation, aspect families, and examples. It is front-loaded with purpose and when-to-use guidance, and every section earns its place given the tool's complexity. Slightly more structure, such as bulleted aspects, could improve scannability but the current prose is not wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a probabilistic forecast tool with five parameters and eight aspects, the description is remarkably complete. It covers when to use it, how to read percentiles, what each aspect contains, interpretation caveats, and examples. Output schema handles return-value details, so nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all five parameters well. The description adds value beyond the schema by providing example payloads and expanding on what each aspect family includes, such as snow accumulation percentiles and severe-weather probabilities. This raises it above the baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: probabilistic forecast guidance for one weather aspect, including percentile ranges, exceedance probabilities, and ensemble spread. It clearly distinguishes this from deterministic forecasts and enumerates the eight aspects it supports. The tool is easy to differentiate from siblings like get_forecast and get_hourly_forecast.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use this tool for questions about odds, ranges, potential, or confidence, and notes that a deterministic forecast value cannot answer such questions. It provides interpretation guidance for percentiles and concrete examples, which effectively routes an agent away from deterministic forecast tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the detailed descriptions generally prevent misselection. A few near-overlapping pairs exist — get_current_conditions vs get_observations, and get_forecast already bundling current conditions, alerts, and outlooks — so some ambiguity remains.

Naming Consistency4/5

The overwhelming majority of tools follow a get_<object>_<modifier> pattern in snake_case, and the non-get tools still use an imperative verb_noun form. The mix of verbs (get, list, describe, find, query, search, reverse) is a minor inconsistency, but the overall pattern is predictable.

Tool Count2/5

At 32 tools, the surface is heavy and exceeds the 25+ threshold for a large tool set. The weather domain justifies much of the breadth, but several tools overlap in scope and could plausibly be consolidated, making the count feel higher than necessary.

Completeness5/5

The tool set comprehensively covers current conditions, forecasts, hourly data, climate, alerts, severe weather, air quality, tropical systems, upper-air soundings, maps, model data, geocoding, and platform status. There are no obvious dead-end workflows, and raw access via query_dataset fills most remaining gaps.

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