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Water Service Coverage — Record Quality Audit + Trailing Mean Series
x402
Water Service Coverage — Record Quality Audit + 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 2026-11-02).
Runs two local analytical reports on water service coverage data: a record quality audit and a trailing mean time series, for up to 200 rows of served/total household data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Water service coverage — Record quality audit + Trailing mean series: Record quality audit + Trailing mean series for supplied water service coverage records (served_households, total_households). 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 nulls, out-of-range values, or schema violations in the served_households and total_households fields; and (2) a trailing mean time series computing rolling averages over the specified window for the chosen field, grouped by region and date.
Example use cases
Monthly water coverage data quality check
“I just pulled 120 rows of monthly water service records for our districts — each row has a region, date, served_households, and total_households. Can you run a quality audit on them and also compute a trailing mean for served_households with a window of 4 so I can spot trends?”
Detecting bad records in household water data
“We received a batch of 80 water coverage records from field teams and I'm worried some have nulls or bad values. Run a record quality audit on served_households and total_households for me — here are all 80 rows with row_id, region, and date included.”
Rolling average for water access reporting
“For our quarterly infrastructure report I need a trailing mean series for total_households across 60 regional water service records spanning the last two years. Use a window of 6 and flag any quality issues you find in the data while you're at it.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-19-010?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously validate the integrity of water service coverage records and compute rolling averages over time, without needing live data lookup, external enrichment, or predictive modeling. It is ideal for batch quality assurance workflows on utility datasets of up to 200 rows where both a data audit and a time-series smoothing report are needed in a single call.
Side effects
- Fewer than 1 or more than 200 rows submitted — validation error
- Invalid date format (not YYYY-MM-DD) — schema rejection
- Missing required fields (row_id, region, date, served_households, total_households) — validation error
- Window size larger than number of rows — trailing mean may return empty or partial results
- Non-numeric values in served_households or total_households — quality audit flags or processing error
- Payment not provided or insufficient — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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