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Humidity Observations — String Normalisation + Category Frequencies
x402
Humidity Observations — String Normalisation + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Runs string normalisation and category frequency analysis on up to 200 rows of humidity observation records (humidity_percent, observation_count) using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Humidity observations — String normalisation + Category frequencies: String normalisation + Category frequencies for supplied humidity observations records (humidity_percent, observation_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 analytical reports for the supplied rows: (1) a string normalisation report that standardises text fields such as station_group, and (2) a category frequency report showing how often each distinct category or value range appears across humidity_percent and observation_count fields. Both reports are generated locally without external lookups.
Example use cases
Weather station humidity distribution audit
“I have humidity readings from two station groups over the past month — can you normalize the string fields and tell me the category frequency breakdown for humidity_percent and observation_count across all 120 rows?”
Quality check on humidity sensor data
“Can you run string normalisation and category frequency analysis on my humidity sensor dataset? It has station_group labels, dates, humidity_percent, and observation_count for about 80 rows and I want to see how the values distribute across categories.”
Summarizing multi-site humidity observations
“I've collected humidity observations from several monitoring sites for Q3 — can you normalize the strings and compute category frequencies for the humidity_percent and observation_count columns across my 150 records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-04-024?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of humidity observation data and need both string normalisation of categorical fields and frequency distribution analysis in a single low-cost call ($0.01 USDC). It is ideal for offline/batch analytical passes on weather station data where no live lookups, predictions, or external actions are needed.
Side effects
- More than 200 rows supplied — request rejected with row limit error
- Missing required fields (row_id, date, humidity_percent, observation_count) cause validation failure
- Invalid date format (not YYYY-MM-DD) triggers schema validation error
- Non-numeric values in humidity_percent or observation_count fields cause parse errors
- Empty rows array returns error due to minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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