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Glama

Smarter Weather

by io.github.smarterweather

Get population exposure

get_population_exposure
Read-onlyIdempotent

National population-exposure headline for a risk-zone outlook product: how many people are inside risk bands at or above min_level. Powers headlines like "~57M people under major heat risk tomorrow". hazard=heat covers NWS HeatRisk days 1-3 (levels: 1 minor, 2 moderate, 3 major, 4 extreme). Pass product_id directly for other risk-zone products. Example: {"hazard": "heat", "min_level": 3}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hazardNoHazard family (expands the day-1..3 product set). Currently: heat (HeatRisk).
min_levelNoMinimum risk level to count (>=). Default 1 (any elevated risk).
product_idNoExplicit risk-zone product ID (overrides hazard), e.g. heatrisk_day1_conus.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_levelYes
summariesYes

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond that: it counts people at or above a minimum risk level, covers only NWS HeatRisk days 1-3, and explains that product_id overrides hazard. No contradiction with annotations exists.

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

Conciseness5/5

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

Two compact sentences plus a JSON example. The core behavior is front-loaded, the hazard scope is defined, and the product_id path is stated without redundancy.

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 an output schema exists, all parameters are documented with 100% schema coverage, and annotations cover safety traits, the description completes the picture: it explains the product context, level semantics, hazard coverage, and how to handle other risk-zone products. Nothing essential is missing.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents hazard, min_level, and product_id with clear semantics. The description adds a worked example and reinforces the override relationship, but it does not materially expand on the schema descriptions.

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: get a national population-exposure headline counting people inside risk bands at or above min_level. It also grounds the purpose with a concrete example headline and separates it from generic forecasting tools by calling out risk-zone outlook products.

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

Usage Guidelines4/5

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

The description gives clear usage context: hazard=heat covers NWS HeatRisk days 1-3, and product_id should be passed for other risk-zone products. It does not explicitly name sibling alternatives or state when not to use this tool, so it stops short of full exclusion guidance.

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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