Air Quality Grounding Data

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 Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as AirQualityGroundingData. 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.

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

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

Connecting

One server URL covers every source 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": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.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.

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.

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{
  "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.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at pm2_5: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

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.

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 Grounding Data

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

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