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Reservoir Observations — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Reservoir Observations — 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 2026-10-22).
Runs three analytical reports on supplied reservoir observation records: a record quality audit, string normalisation, and a trailing mean series computation, using local analytical engines with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Reservoir observations — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied reservoir observations records (stored_m3, capacity_m3). 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: (1) a record quality audit highlighting issues with the supplied reservoir observation rows, (2) a string normalisation report cleaning station_group and other string fields, and (3) a trailing mean series for the specified numeric field (stored_m3 or capacity_m3) using the given window size — all computed locally without external calls.
Example use cases
Reservoir data quality check before reporting
“I have 50 rows of reservoir observations from multiple stations — can you audit them for quality issues, normalise the station group strings, and give me a trailing mean series with a window of 5 for stored_m3? Here's the data: [row-1, north-dam, 2024-01-01, 800000, 2000000] through row-50.”
Detect anomalies in water storage time series
“Please run a record quality audit and compute a trailing mean series with window size 7 for capacity_m3 on these 30 reservoir readings — I want to spot any outliers or data issues before I hand them off to the engineering team.”
Normalise station labels across merged reservoir datasets
“We merged reservoir data from two sources and the station_group labels are inconsistently formatted — can you run string normalisation plus a quality audit on these 120 rows (stored_m3 and capacity_m3 included) and also give me a trailing mean with a window of 4 for stored_m3?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-19-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytical operations — quality audit, string normalisation, and trailing mean series — on reservoir observation data (stored_m3, capacity_m3) in a single cost-effective call. Ideal for pre-processing reservoir datasets before reporting or ingestion, when no live data lookup, prediction, or external action is required. Best suited for batches of up to 200 rows where all three report types are needed simultaneously.
Side effects
- Exceeding 200 rows returns an error — batch must be split
- Missing required fields (row_id, station_group, date, stored_m3, capacity_m3) causes validation failure
- Invalid date format (not YYYY-MM-DD) causes rejection
- Window size outside 2–200 range causes parameter error
- Null values in numeric fields may affect trailing mean computation
- Payment not included or insufficient USDC causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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