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Null Coverage Analytics — String Normalisation + Numeric Row Ordering
x402
Null Coverage Analytics — 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 2026-10-10).
Normalises string fields and orders rows numerically for null coverage records (missing_values, total_values), producing 2 independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied null coverage records (missing_values, total_values). 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: (1) a string normalisation report for the specified field across the submitted rows, and (2) a numeric row ordering report sorted by the selected numeric field (missing_values or total_values). Results are computed locally from supplied data only, with no external lookups or predictions. Covers up to 200 rows per call.
Example use cases
Data quality audit for ETL pipeline
“I need to audit the null coverage in my ETL pipeline output — can you normalise the dataset group names and order the rows by missing_values for these 5 records: row-1 sales-data 2026-11-01 missing 12 of 500, row-2 sales-data 2026-11-02 missing 0 of 480, row-3 inventory 2026-11-03 missing 45 of 600, row-4 inventory 2026-11-04 missing 3 of 590, row-5 sales-data 2026-11-05 missing 8 of 510?”
Monthly completeness report by dataset group
“Give me two reports — one normalising group names and one ordering rows numerically by total_values — for my monthly completeness check across these rows: row-1 group-alpha 2026-12-01 missing 5 of 1000, row-2 group-beta 2026-12-02 missing 20 of 800, row-3 group-alpha 2026-12-03 missing 0 of 950.”
Identifying worst null coverage records
“I want to find which of my dataset records have the highest missing values — can you normalise the dataset_group strings and sort all rows by missing_values descending? Here are my 4 rows: row-1 crm-leads 2026-09-01 missing 300 of 1000, row-2 crm-contacts 2026-09-02 missing 150 of 800, row-3 crm-leads 2026-09-03 missing 500 of 1000, row-4 crm-contacts 2026-09-04 missing 50 of 800.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-025?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, offline null coverage analytics — specifically string normalisation of group labels and numeric ordering of rows by missing or total value counts — for datasets of up to 200 rows, without requiring any external data lookup, live prediction, or professional assessment. It is ideal for data quality pipelines, ETL audit workflows, or completeness reporting where you want two structured reports for a flat cost of $0.01 USDC.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) causes rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Invalid field enum value (not missing_values or total_values) causes error
- Empty rows array (minItems:1 violated) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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