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Coordinate Quality Checks — Record Quality Audit, String Normalisation & Secondary-Measure Weighted Mean
x402
Coordinate Quality Checks — Record Quality Audit, String Normalisation & Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-04).
Runs three analytical reports (record quality audit, string normalisation, and secondary-measure weighted mean) on up to 200 rows of coordinate quality check data (checked_points, flagged_points).
Price / call$0.01
Success—
StatusUnknown
How this service works
Coordinate quality checks — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied coordinate quality checks records (checked_points, flagged_points). 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 rows: (1) a record quality audit identifying completeness and consistency issues, (2) a string normalisation report for station_group and text fields, and (3) a secondary-measure weighted mean calculation for the selected numeric field (checked_points or flagged_points). All computed locally with no external lookups.
Example use cases
Station group data quality audit
“I have daily coordinate check records for two station groups — alpha and beta — covering the past week, each row has a checked_points count and a flagged_points count. Can you run the full quality audit, string normalisation, and weighted mean on checked_points across all those rows?”
Flagged-point weighted mean for anomaly review
“We've collected 150 rows of coordinate inspection results with checked and flagged point counts per station group. Please compute the secondary-measure weighted mean on flagged_points and include the record quality audit and string normalisation reports.”
Pre-submission data validation for coordinate survey
“Before we submit our coordinate survey batch to the pipeline, run the record quality audit, string normalisation, and weighted mean analysis on checked_points for these 50 station rows so we can catch any data issues.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-23-205?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have batches of up to 200 coordinate quality check records and need three simultaneous analytical reports (audit, normalisation, and weighted mean) in a single paid call. Prefer it over general-purpose analytics when your data specifically involves station_group, checked_points, and flagged_points fields and you need deterministic, locally-computed results with no external data dependencies.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, date, checked_points, flagged_points) — row-level validation failure
- Invalid date format not matching YYYY-MM-DD pattern — schema validation error
- Null values in both checked_points and flagged_points — may produce incomplete weighted mean
- Invalid field enum value (not checked_points or flagged_points) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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