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Water Service Coverage — String Normalisation + Secondary-Measure Weighted Mean
x402
Water Service Coverage — String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Normalises region/date strings and computes a secondary-measure weighted mean across water service coverage records (served_households, total_households) for up to 200 rows, returning two analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Water service coverage — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean 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
Two analytical reports per call: (1) a string-normalised version of the region and date fields across all submitted rows, and (2) a secondary-measure weighted mean for the chosen field (served_households or total_households) computed across all rows, using the other household field as the secondary weighting measure.
Example use cases
Regional water coverage reporting
“I have 120 rows of household water coverage data across 8 regions with messy region names and inconsistent dates — can you normalise the strings and compute a weighted mean for served_households so I can summarise coverage levels per region?”
Utility audit data cleanup
“Our water utility audit dataset has 50 records with served_households and total_households values; can you run string normalisation on the region and date fields and give me a secondary-measure weighted mean for total_households across all rows?”
Multi-region coverage comparison
“I need to compare water service coverage across group-a and group-b for Q4 2026 — I have 80 rows with served and total household counts. Can you normalise the region labels and dates, then calculate the weighted mean for served_households?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-19-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular water service coverage data (served_households, total_households) and need both string normalisation of region/date fields and a secondary-measure weighted mean in a single batch call of up to 200 rows, without requiring any live data lookup or external prediction.
Side effects
- Missing required fields (row_id, region, date, served_households, total_households) cause validation errors
- More than 200 rows submitted returns an error (maxItems exceeded)
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Null values for both household fields may produce undefined weighted mean
- Payment failure or insufficient USDC balance blocks the call
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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