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Air Quality Observations – Numeric Row Ordering
x402
Air Quality Observations – Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Sorts up to 200 air quality observation records by a chosen numeric field (PM2.5 or humidity) and returns a ranked row order with descriptive analytics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Air quality observations - Numeric row ordering: Numeric row ordering for Air quality observations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded numeric row ordering report.
Output
Returns a numerically ordered ranking of the submitted air quality observation rows based on the chosen field (PM2.5 or humidity_percent), including local descriptive analytics such as ordering positions and summary statistics — no external enrichment or forecasting is applied.
Example use cases
Ranking stations by peak pollution
“I have readings from 10 air quality stations over the past week — can you sort all 70 rows by PM2.5 so I can see which station-days had the worst pollution?”
Humidity-based ordering for sensor audit
“Sort these 50 air quality sensor records by humidity percentage so I can review them in ascending order and spot any outliers.”
Daily air quality comparison for report
“I've got 30 daily air quality observations from stations downtown and uptown — please rank them by PM2.5 so I can identify the top 5 worst days for our environmental report.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-air-quality-observations-sort?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 air quality observation records and need them sorted and ranked by either PM2.5 or humidity — purely as a local, self-contained analytic operation with no external data enrichment, forecasting, or cross-dataset joins required. It is ideal for quick descriptive ranking tasks within a bounded dataset.
Side effects
- Empty or missing rows array returns validation error
- Row count exceeding 200 items rejected with maxItems error
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Unrecognized field value outside enum ['pm25','humidity_percent'] returns error
- Null numeric values may affect sort ordering or produce warnings
- Missing required fields (row_id, station, date, pm25, humidity_percent) cause schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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