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Transaction Fee Observations — Record Quality Audit, String Normalisation & Exact Row Deduplication
x402
Transaction Fee Observations — Record Quality Audit, String Normalisation & Exact Row Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on up to 200 transaction fee observation rows: record quality audit, string normalisation, and exact row deduplication.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transaction fee observations — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied transaction fee observations records (fee_units, transaction_count). 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 submitted transaction fee observation rows: (1) a record quality audit flagging missing, null, or out-of-range values; (2) a string normalisation report standardising text fields such as instrument_group; (3) an exact row deduplication report identifying identical rows. All processing is performed locally with no external lookups or live data.
Example use cases
Pre-pipeline fee data cleanup
“I'm about to load this batch of transaction fee observations into our data warehouse — can you first run a quality audit, string normalisation, and deduplication check on these 120 rows (each has a row_id, instrument_group like 'equities' or 'fx', date, fee_units, and transaction_count) so I can catch any issues before ingestion?”
Reconciliation audit for finance team
“Our finance team exported 80 rows of transaction fee data from two different systems and suspects there are duplicates and inconsistent group labels — can you run all three reports: record quality audit, string normalisation, and exact row deduplication on these rows?”
Monthly fee report data validation
“Before I finalise the monthly transaction fee report, please check these 200 fee observation rows for quality issues, normalise the instrument_group strings, and flag any exact duplicate rows — fields include date, fee_units, transaction_count, and instrument_group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-03-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of: record quality auditing, string field normalisation, and exact duplicate detection on transaction fee observation data (fee_units, transaction_count) in a single API call at low cost ($0.01 USDC for up to 200 rows). It is ideal for batch pre-processing before loading into a data warehouse or reporting pipeline. Prefer it over general-purpose data quality tools when your data specifically contains instrument_group, fee_units, and transaction_count fields and you want bundled, local analytical reports with no external data calls.
Side effects
- Submission of more than 200 rows will be rejected (maxItems constraint)
- Missing required fields (row_id, instrument_group, date, fee_units, transaction_count) will cause validation errors
- Invalid date format (non-YYYY-MM-DD) will fail pattern validation
- Numeric values outside ±1,000,000,000,000 will be rejected
- Empty rows array (minItems: 1) will be rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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