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Metadata Completeness — Base-100 Series + Secondary-Measure Weighted Mean
x402
Metadata Completeness — Base-100 Series + 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 2026-10-23).
Computes a base-100 indexed time series and a secondary-measure weighted mean for metadata completeness records (present_fields vs expected_fields) across up to 200 rows, producing two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Metadata completeness — Base-100 series + Secondary-measure weighted mean: Base-100 series + Secondary-measure weighted mean 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 base-100 indexed time series normalizing the selected field (present_fields or expected_fields) relative to the value at the specified base_index row, and (2) a secondary-measure weighted mean computed across all supplied rows. Both reports are generated locally from the supplied data with no external lookups.
Example use cases
Data pipeline quality trending
“I'm tracking metadata completeness for my ETL pipeline across two site groups over the past week — can you index the present_fields counts to a base-100 series starting at the first row and also give me a weighted mean? I have 5 rows: prod-1 for pipeline-a on 2026-11-01 with 80 present and 100 expected, prod-2 for pipeline-b on 2026-11-02 with 60 present and 100 expected, prod-3 for pipeline-a on 2026-11-03 with 90 present and 100 expected, prod-4 for pipeline-b on 2026-11-04 with 75 present and 100 expected, prod-5 for pipeline-a on 2026-11-05 with 85 present and 100 expected.”
Schema coverage comparison across teams
“My two engineering teams fill out metadata schemas differently — can you run the base-100 series and weighted mean analysis on expected_fields for these 6 rows so I can compare coverage trends? team-alpha on 2026-09-01 expected 50, team-beta on 2026-09-02 expected 40, team-alpha on 2026-09-03 expected 55, team-beta on 2026-09-04 expected 45, team-alpha on 2026-09-05 expected 60, team-beta on 2026-09-06 expected 50. Use base index 0.”
Monthly metadata audit report
“I need two reports on my monthly metadata audit — a base-100 indexed time series and a secondary weighted mean — for present_fields across these 8 records: audit-1 through audit-8 all in group-main, dates from 2026-01-01 to 2026-08-01 monthly, with present_fields values of 120, 135, 110, 150, 140, 160, 145, 170 and expected_fields all set to 200. Anchor the base index at row 0.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-05-166?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to both index a metadata completeness time series to a base-100 scale AND compute a weighted mean in a single call for up to 200 rows. It is ideal for data quality monitoring, schema coverage audits, and ETL pipeline analytics where you need two complementary statistical views (trend indexing + central tendency) from the same dataset without external dependencies or live data lookups.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, present_fields, expected_fields) — validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- base_index out of range (exceeds row count minus 1) — index error
- null values in present_fields or expected_fields may affect weighted mean calculation
- Division by zero risk if expected_fields is 0 for completeness ratio computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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