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Join Coverage Record Quality Audit – String Normalisation & Missing-Value Fill
x402
Join Coverage Record Quality Audit – String Normalisation & Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Audits join coverage records (matched_rows, source_rows) for quality issues, normalises strings, and fills missing values using zero/mean/median imputation — three reports for up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied join coverage records (matched_rows, source_rows). 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: (1) a record quality audit flagging malformed, inconsistent, or anomalous rows; (2) a string normalisation report showing standardised dataset_group and other string fields; and (3) a missing-value fill report with imputed values for the chosen field (matched_rows or source_rows) using the selected method (zero, mean, or median). All computed locally from the supplied data with no external lookups.
Example use cases
ETL pipeline join quality check
“I have a batch of 50 join coverage records from our ETL pipeline with possible nulls in matched_rows — can you run a full quality audit, normalise the dataset_group strings, and fill missing matched_rows with median imputation?”
Data warehouse coverage gap detection
“We're seeing inconsistencies in our data warehouse joins. Can you audit these 120 join coverage rows for quality problems, clean up the string fields, and use mean imputation to fill any missing source_rows values?”
Pre-merge dataset validation
“Before we merge these two datasets, please check these 30 join coverage records for record quality issues, normalise the strings, and fill any null source_rows using zero fill so we can see exactly where the gaps are.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data-quality operations — record audit, string normalisation, and missing-value imputation — applied together to join coverage metrics (matched_rows and source_rows) in a single paid call. It is best suited for small-to-medium batches (up to 200 rows) where you want a cost-effective combined report rather than calling separate services. Prefer it over generic data-cleaning tools when your data specifically consists of join coverage records with row-match statistics grouped by dataset and date.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause schema rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is not accepted (minItems: 1)
- Invalid enum value for method or field triggers a 400-level error
- Payment of 0.01 USDC must be included via x402 protocol or request is rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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