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Air Quality

air_quality
Read-onlyIdempotent

Get the current air quality for a set of coordinates: an air quality index (AQI, 1=Good to 5=Very Poor) plus pollutant component concentrations (CO, NO2, O3, PM2.5, PM10, etc.). Example: air_quality({ lat: 51.5, lon: -0.12 })

Input Schema

NameRequiredDescriptionDefault
latYesLatitude.
lonYesLongitude.
_apiKeyNoOptional — your own OpenWeatherMap API key for higher limits; omit to use the shared Pipeworx key.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
      -  {
      -    "_apiKey": "your-openweather-api-key",
      -    "lat": 51.5074,
      -    "lon": -0.1278
      -  }
      -]New value: +[
      +  {
      +    "lat": 51.5074,
      +    "lon": -0.1278
      +  }
      +]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "_apiKey": "your-openweather-api-key",
      +    "lat": 51.5074,
      +    "lon": -0.1278
      +  }
      +]
  3. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), so the description's job is lighter. It earns credit by disclosing the return shape — AQI normalized 1=Good to 5=Very Poor and the pollutant component list — which annotations cannot express. It omits rate-limit implications of the shared vs personal API key, but the schema handles that.

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?

Two sentences, front-loaded with the verb and resource, with no filler. The inline example slightly duplicates the schema's own examples array, a minor redundancy rather than waste.

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?

No output schema exists, so describing the returned AQI scale and pollutant set is genuinely necessary and it is done well. Remaining gap is modest: no mention of units, null-of-data handling, or what happens when coordinates are over water, but an agent has enough to call it correctly.

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 lat/lon and _apiKey are already documented; the description only repeats coordinate usage via an example with concrete values. Baseline 3 applies since the schema carries the param burden and the description adds no format or constraint detail beyond it.

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

Purpose4/5

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

States a specific verb and resource ('Get the current air quality') scoped to coordinates, and enumerates the returned data (AQI on a 1-5 scale plus pollutant concentrations). It implicitly separates itself from weather siblings like current_weather/forecast by naming air quality, though it never explicitly contrasts them.

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 coordinate framing and worked example imply when to call it, but there is no explicit guidance on when not to, nor any pointer to alternatives such as current_weather for temperature or forecast for future AQI. Usage is inferable rather than stated.

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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