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Coordinate Quality Checks — Record Audit + String Normalisation + Numeric Interval Filter
x402
Coordinate Quality Checks — Record Audit + String Normalisation + Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Audits, normalises, and filters up to 200 rows of coordinate quality check records (checked_points, flagged_points) using three independent local analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Coordinate quality checks — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter 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 supplied rows: (1) a record quality audit highlighting data issues, (2) a string normalisation report cleaning station_group and similar text fields, and (3) a numeric interval filter report showing which rows fall within the specified minimum–maximum range for the chosen field (checked_points or flagged_points). Up to 200 rows processed per call.
Example use cases
Station network data quality review
“I have 120 rows of coordinate quality check records from multiple station groups with checked_points and flagged_points values — can you run the record quality audit, string normalisation, and numeric interval filter on them, keeping checked_points between 0 and 1000?”
Flagged points outlier detection
“Run a quality audit and numeric interval filter on my coordinate dataset — 80 rows with station_group, date, checked_points, and flagged_points — filtering the flagged_points field to only rows between 0 and 50 so I can spot outliers.”
Coordinate monitoring data clean-up
“I need to clean up my coordinate monitoring records before analysis — can you normalise the station_group strings and filter out any rows where checked_points falls outside the range 10 to 200, across these 30 rows I'll supply?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-23-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular coordinate quality check data (checked_points, flagged_points per station group and date) and need all three analytical operations — record quality auditing, string normalisation, and numeric range filtering — in a single low-cost call. It is ideal for pre-analysis data cleaning of sensor or monitoring station datasets up to 200 rows. Prefer alternatives if you need live lookups, external data enrichment, prediction, or more than 200 rows in one batch.
Side effects
- Exceeding 200 rows causes a validation error
- Missing required fields (row_id, date, checked_points, flagged_points) will reject the row or the whole request
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- minimum greater than maximum produces an empty or nonsensical filter result
- Null values in checked_points or flagged_points may be excluded from numeric filter reports
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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