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Building Footprint Observations — Record Quality Audit, String Normalisation & Numeric Row Ordering
x402
Building Footprint Observations — Record Quality Audit, String Normalisation & 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 2026-10-30).
Audits record quality, normalises strings, and orders rows numerically for up to 200 building footprint observation records (footprint_m2, height_m) across 3 independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied building footprint observations records (footprint_m2, height_m). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports for the supplied building footprint observation rows: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed fields; (2) a string normalisation report for station_group and similar text fields; and (3) a numerically ordered row listing sorted by the selected field (footprint_m2 or height_m). All processing is local with no external lookups.
Example use cases
Pre-analysis data quality check
“I have 120 building footprint observation records with footprint_m2 and height_m columns and I need to check them for quality issues, normalise the station_group strings, and sort them by height_m before I start my analysis — can you run all three reports?”
Validating surveyor-submitted building data
“Our field surveyors submitted 85 building measurement rows and I need to audit record quality, clean up the station group labels, and order everything by footprint_m2 — can you give me those three reports?”
Cleaning building dataset before database import
“Before I import these 200 building footprint observation rows into our database, can you run a quality audit, normalise the station_group strings, and order the records numerically by footprint_m2 so I can spot any issues?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC) set of three data quality operations — quality audit, string normalisation, and numeric ordering — on building footprint observations (footprint_m2, height_m) with up to 200 rows, entirely offline without any live data lookups or external predictions.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, footprint_m2, height_m, station_group) causes schema rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside the allowed range trigger schema errors
- Empty rows array is rejected (minItems: 1)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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