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Building Footprint Observations — Record Quality Audit & String Normalisation
x402
Building Footprint Observations — Record Quality Audit & String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Runs a record quality audit and string normalisation on up to 200 building footprint observation rows (footprint_m2, height_m), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied building footprint observations records (footprint_m2, height_m). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent analytical reports per batch: (1) a record quality audit highlighting issues such as missing values, out-of-range measurements, and malformed dates across the supplied footprint_m2 and height_m fields; and (2) a string normalisation report showing standardised versions of string fields like station_group. Both reports cover up to 200 input rows processed locally without external lookups.
Example use cases
Pre-analysis data quality check
“I've got 120 building footprint observation rows with footprint_m2 and height_m values and I need to audit them for quality issues before I run my analysis — can you produce both the record quality audit and the string normalisation report for all of them?”
Cleaning building survey dataset
“My team collected building survey data with station_group labels and footprint measurements across 80 sites — can you normalise the station_group strings and flag any record quality problems in the footprint_m2 and height_m columns?”
Validating imported construction records
“I just imported 200 rows of building height and footprint observations from a third-party source and I'm worried about inconsistent strings and bad data — please run a full record quality audit and string normalisation on all of them.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 building footprint observation records (with footprint_m2 and height_m fields) and need both a data quality audit and string normalisation in a single call, without any live lookup, prediction, or external data enrichment. It is ideal for pre-processing pipelines that need to validate and clean building measurement data locally before downstream analysis.
Side effects
- Exceeding 200 rows causes a validation error rejecting the request
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) on any row causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema validation failure
- row_id exceeding 80 characters or being empty causes a validation error
- Numeric values outside the allowed range cause schema rejection
- Malformed JSON body returns an error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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