Air Quality Skill

Overview

Air Quality works by retrieving the air quality data from various reliable sources and providing it in a structured format. You can expect accurate and up-to-date information.

Live Test Air Quality Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as AirQualitySkill. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

Tool call

{
  "name": "AirQualitySkill",
  "arguments": {
    "city": "San Francisco"
  }
}

You do not name the tool yourself; the model picks it. Asking about San Francisco in the terms this skill covers is enough for it to reach for AirQualitySkill on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every skill in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervekit": {
      "url": "https://api.vervekit.com/v1/mcp"
    }
  }
}

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

Argument	Type	Description
`city`Required	string	The city name for which you want to get the air quality data (e.g., New York)

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

Result

{
  "status": "ok",
  "error": null,
  "data": {
    "pm2_5": 16.75,
    "pm10": 18.85,
    "carbonMonoxide": 387.85,
    "ozone": 9,
    "nitrogenDioxide": 38.55,
    "sulfurdioxide": 5.95,
    "usEpaIndex": 2,
    "gbDefraIndex": 2,
    "recommendation": "The air quality in San Francisco is good. It is safe to go outside.",
    "city": "San Francisco"
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Field	Type	Example	Description
`pm2_5`	number	`16.75`	Fine particulate matter concentration in micrograms per cubic meter
`pm10`	number	`18.85`	Coarse particulate matter concentration in micrograms per cubic meter
`carbonMonoxide`Premium	number	`387.85`	Carbon monoxide concentration level in parts per billion
`ozone`Premium	number	`9`	Ground-level ozone concentration in parts per billion
`nitrogenDioxide`Premium	number	`38.55`	Nitrogen dioxide concentration level in parts per billion
`sulfurdioxide`Premium	number	`5.95`	Sulfur dioxide concentration level in parts per billion
`usEpaIndex`Premium	number	`2`	US EPA air quality index rating from one to six scale
`gbDefraIndex`Premium	number	`2`	UK DEFRA air quality index rating from one to ten scale
`recommendation`	string	`The air quality in San Francisco is good. It is safe to go outside.`	Air quality assessment and health recommendation for the city
`city`	string	`San Francisco`	The city name for which air quality data was retrieved

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This skill is not part of VerveKit. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 10 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

HVAC Air Intake Automation: Smart thermostat controllers read local PM2.5 and PM10 concentrations to automatically switch ventilation systems to internal recirculation during high pollution spikes.
Outdoor Workout Planning: Fitness mobile apps show runners actionable health recommendations and particulate ratings by city before they start an outdoor training session.
Property Environmental Profiles: Display local particulate measurements and health advice on real estate listings so buyers can evaluate neighborhood environmental quality.
School Sports Scheduling: When particulate counts exceed safe thresholds, athletic directors consult real-time pollution readings and health recommendations to move student practices indoors.

Other ways to use Air Quality Skill

Set up Air Quality Skill on VerveKit, or reach the same source a different way. Your VerveKit account and credits work on all of them — one key, one balance.

Call it as a REST APIOne HTTPS endpoint and an x-api-key header, with SDKs for Node, Python and .NET.APIVerve →Reference →

Google Sheets or ExcelA =VERVE() formula fills a column — no script, no export, recalculates in place.VerveSheets →Reference →

Ground an agent on itA cited, machine-checkable fact your model can't produce on its own.VerveContext →Reference →