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Record Routing Analytics — Quality Audit, String Normalisation & Base-100 Series
x402
Record Routing Analytics — Quality Audit, String Normalisation & Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three independent analytical reports (record quality audit, string normalisation, and base-100 index series) on supplied record routing data containing accepted and rejected record counts, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Record routing — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series for supplied record routing records (accepted_records, rejected_records). 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
Returns three independent analytical reports for the supplied rows: (1) a record quality audit flagging data issues across accepted_records and rejected_records, (2) a string normalisation report for text fields such as dataset_group and row_id, and (3) a base-100 index series for the selected numeric field (accepted_records or rejected_records) anchored at the specified base_index row. All computed locally with no external lookups.
Example use cases
Daily pipeline quality audit
“I have 50 rows of daily record routing data from my ETL pipeline — each row has a date, a dataset group, and counts of accepted and rejected records. Can you run a quality audit, string normalisation, and base-100 index series on them, indexed on accepted_records from row 0?”
Rejection rate anomaly investigation
“My team suspects our record router is rejecting unusually high volumes on certain days. Can you run the three-report analytics — quality audit, string normalisation, and base-100 series — on these 80 routing rows so I can spot outliers in the rejected_records field, starting the index at row 0?”
Monthly dataset group comparison
“I want to compare record routing quality across four dataset groups for October. Here are 120 rows with dates, group names, accepted counts, and rejected counts — please run the quality audit, string normalisation, and base-100 index reports on accepted_records with base index at 0.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-19-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical passes — quality audit, string normalisation, and base-100 indexing — on record routing data (accepted/rejected record counts by group and date) in a single low-cost call. It is ideal for data pipeline monitoring, ETL quality checks, and time-series indexing of routing metrics without requiring external data sources or live lookups. Not suitable if you need live data enrichment, predictive modelling, or more than 200 rows per call.
Side effects
- Exceeding 200 rows causes request rejection
- Missing required fields (row_id, date, accepted_records, rejected_records) returns a validation error
- Invalid date format (non-YYYY-MM-DD) triggers schema validation failure
- base_index out of range (0–199) returns an error
- Null numeric values in accepted_records or rejected_records may affect index computation
- Malformed JSON body returns HTTP 400
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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