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Building Footprint Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes
x402
Building Footprint Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes 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 detects adjacent row changes for up to 200 rows of building footprint observation data (footprint_m2, height_m), returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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 submitted building footprint rows: (1) a record quality audit identifying missing values, format issues, and data integrity problems; (2) a string normalisation report standardising station_group and other string fields; (3) an adjacent row changes report highlighting where footprint_m2 or height_m values differ between consecutive rows. All computed locally with no external lookups.
Example use cases
Building dataset pre-processing audit
“I have 120 rows of building footprint observations from our urban survey, each with a footprint_m2 and height_m. Can you audit their quality, normalise any string inconsistencies in station_group labels, and flag where footprint_m2 changes between adjacent rows?”
Construction project data validation
“Before we submit our building measurements to the planning authority, can you run a quality audit and adjacent row change detection on these 80 rows of height_m data across our three station groups, covering dates from 2026-09-01 to 2026-09-30?”
Real estate footprint inventory check
“We've got 200 building footprint records from our property inventory — can you check them for record quality issues, normalise the station_group strings, and show us where the footprint_m2 values shift between consecutive rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 building footprint observation rows and need three bundled local analytical reports — quality audit, string normalisation, and adjacent row change detection — in a single low-cost call ($0.01 USDC). It is ideal for pre-processing building measurement datasets before downstream analysis, submission, or storage, and when no live lookup, prediction, or external data enrichment is needed.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) in any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Payment not provided or insufficient — 402 Payment Required
- field enum value not one of footprint_m2 or height_m — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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