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Transaction Fee Observations — Record Quality Audit + IQR Outlier Flags
x402
Transaction Fee Observations — Record Quality Audit + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Audits up to 200 rows of transaction fee observation records for data quality issues and flags IQR-based outliers across fee_units or transaction_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transaction fee observations — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied transaction fee observations records (fee_units, transaction_count). 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
Returns two independent reports: (1) a record quality audit identifying issues such as missing values, malformed dates, or invalid entries per row; and (2) IQR outlier flags marking which rows fall outside the interquartile range for the selected numeric field (fee_units or transaction_count). Both reports are computed locally without external lookups.
Example use cases
Audit daily fee data before reporting
“I need to quality-check my transaction fee dataset before I send it to the finance team — it has 80 rows with date, instrument_group, fee_units, and transaction_count. Can you run a record quality audit and flag any IQR outliers on fee_units?”
Detect anomalous transaction counts
“Something looks off in my transaction count data — can you run an IQR outlier analysis on my transaction_count field across these 120 rows of fee observations and also audit them for any data quality problems?”
Validate instrument group fee records
“Before I load this into the data warehouse, can you audit these 200 rows of instrument group fee records for quality issues and IQR-based outliers on fee_units? Each row has a row_id, instrument_group, date, fee_units, and transaction_count.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-03-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of transaction fee observation data and need both a structural data quality audit and IQR-based statistical outlier detection in a single low-cost call ($0.01 USDC). It is ideal for pre-load validation of fee datasets, catching bad rows before downstream reporting, or quickly flagging anomalous fee or count observations without any live external data dependency.
Side effects
- Exceeding 200 rows returns an error — batch must be split
- Missing required fields (row_id, date, instrument_group) causes row rejection
- Invalid date format (non-YYYY-MM-DD) triggers validation error
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Field enum value not in ['fee_units','transaction_count'] causes schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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