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Air Quality – Latest

air_quality_latest

Get current air quality readings for a location using coordinates or place name. Includes AQI, CO, NO2, ozone, PM10, PM2.5, SO2, dominant pollutant, and category.

Instructions

Returns the latest air quality reading for a location: AQI plus CO, NO2, ozone, PM10, PM2.5, and SO2 concentrations, with the dominant pollutant and category. Returns a single record for the current hour, or no data if none is available for that location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude of the location, -90 to 90. Required if place is not provided.
lngNoLongitude of the location, -180 to 180. Required if place is not provided.
placeNoPlace or city name, e.g. "Bengaluru". Required if lat/lng are not provided. Never send both place and lat/lng.
localeNoIf true, the response includes a localTime field alongside each record.
aqiStandardNoAQI standard to calculate against. Defaults to EPA.

Schema Changelog

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

  1. First observedv2.0.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It usefully discloses that the tool returns a single record for the current hour, may return no data, and includes dominant pollutant and category. It could add more detail about response shape or edge cases, but it covers the most important behavioral traits.

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?

The description is two sentences with no filler. It front-loads the core purpose, then immediately lists the returned measurements and the single-record/no-data behavior. Every sentence earns its place.

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

Completeness4/5

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

There is no output schema, so a good description must convey what the tool returns; this one does, including pollutant set and cardinality. Combined with full parameter documentation in the schema, the definition gives an agent enough to select and call the tool correctly, though a bit more detail on response structure would be helpful.

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 description coverage is 100%, so the schema already documents all five parameters including lat/lng/place mutual exclusivity, locale behavior, and aqiStandard enum. The description adds no parameter-level meaning, but the baseline of 3 is appropriate because the schema does the heavy lifting.

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 ('Returns') and resource ('latest air quality reading for a location'), enumerates the exact pollutants and derived fields, and clarifies it returns a single current-hour record or no data. This clearly distinguishes it from the forecast and weather/pollen siblings.

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

Usage Guidelines3/5

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

The 'latest' versus forecast distinction is implied by the sibling names and by the explicit 'current hour' phrasing, so an agent can infer when to use it. However, it does not explicitly state 'use this for current conditions, use air_quality_forecast for predictions' or list any exclusion criteria.

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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