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Metadata Completeness Daily Sum Series with Missing-Value Fill
x402
Metadata Completeness Daily Sum Series with Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Computes a daily sum time series for metadata completeness fields (present_fields, expected_fields) and fills in missing values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Metadata completeness — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill for supplied metadata completeness records (present_fields, expected_fields). 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: (1) a daily sum time series aggregating the selected field (present_fields or expected_fields) by date, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports cover up to 200 input rows and are computed locally without external lookups.
Example use cases
Daily completeness trend by site group
“I have 150 daily metadata completeness records across two site groups — group-a and group-b — each with present_fields and expected_fields counts. Can you generate a daily sum series for present_fields and fill any missing days using median imputation?”
Zero-fill gaps in expected field counts
“My metadata completeness dataset has some null expected_fields values across dates from 2026-10-01 to 2026-10-31 for about 80 rows. Please compute the daily sum series for expected_fields and fill all the missing values with zero.”
Mean imputation for data quality audit
“I'm auditing metadata completeness for our data pipeline — I have 200 rows with site_group, date, present_fields, and expected_fields. Can you run the daily sum series on present_fields and use mean fill for any missing values?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-05-145?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compute a daily aggregate sum time series and simultaneously handle missing-value imputation for metadata completeness records (present_fields / expected_fields), in a single $0.01 call covering two reports. Prefer it over general analytics tools when your data is already structured with row_id, site_group, and date, and you need lightweight, local computation without external data lookups or prediction.
Side effects
- More than 200 rows submitted — API rejects input exceeding the maxItems limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, date, present_fields, expected_fields) result in request rejection
- present_fields or expected_fields values outside ±1,000,000,000,000 fail numeric range validation
- Empty rows array (minItems: 1 not met) causes rejection
- Invalid field enum value (not 'present_fields' or 'expected_fields') causes rejection
- Invalid method enum (not 'zero', 'mean', or 'median') causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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