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Field Survey Coverage Analyzer — Record Quality Audit, String Normalisation & Trailing Mean Series
x402
Field Survey Coverage Analyzer — Record Quality Audit, String Normalisation & Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Runs three local analytical reports on field survey coverage data: a record quality audit, string normalisation, and a trailing mean time series — all without external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Field survey coverage — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied field survey coverage records (surveyed_m2, target_m2). 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 issues such as nulls, out-of-range values, and malformed fields; (2) a string normalisation report cleaning station_group and other string fields; and (3) a trailing mean time series computed over the chosen numeric field (surveyed_m2 or target_m2) with the specified window size — all derived locally without external data sources.
Example use cases
Environmental survey data quality check
“I have 150 rows of field survey coverage records with surveyed_m2 and target_m2 values across several station groups — can you audit the record quality, normalise the station group strings, and compute a trailing mean with a window of 5 for surveyed_m2?”
Trailing mean trend analysis for coverage monitoring
“Compute a trailing mean series with window size 4 on the target_m2 field for these 80 survey records grouped by station — also give me the quality audit and string normalisation reports at the same time.”
Cleaning and validating survey batch before reporting
“Before I submit my quarterly survey report, can you run all three reports — record quality audit, string normalisation, and trailing mean with window 2 — on these 60 field coverage rows so I can catch any bad data or inconsistent labels?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-22-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously audit data quality, normalise string fields, and compute a rolling mean on field survey coverage data (surveyed_m2/target_m2) in a single call. It is ideal for batch preprocessing up to 200 survey records before downstream reporting, without any need for external lookups or live data. Prefer it over generic analytics tools when working specifically with structured survey coverage datasets that have station_group and area measurement columns.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, station_group, date, surveyed_m2, target_m2) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema rejection
- Window size outside 2–200 range — parameter error
- Both numeric fields null for all rows — computation may return empty series
- Payment of 0.01 USDC not completed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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