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News Archive Completeness — String Normalisation + Secondary-Measure Weighted Mean
x402
News Archive Completeness — 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 2026-11-02).
Computes string normalisation and secondary-measure weighted mean analytics on up to 200 rows of news archive completeness data (indexed_items, expected_items), returning 2 independent reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
News archive completeness — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied news archive completeness records (indexed_items, expected_items). 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 independent analytical reports per call: (1) string-normalised values for the selected field across supplied rows, and (2) a secondary-measure weighted mean computed over the indexed_items and expected_items columns — both derived locally from the supplied data with no external lookups.
Example use cases
Daily archive gap audit by channel
“I need to check how complete our news archive indexing is across two channel groups — run string normalisation and a weighted mean on this batch of 8 rows from last week, using indexed_items as the field, so I can see where we're falling short against expected counts.”
Cross-date completeness trend report
“Can you compute a secondary-measure weighted mean over these 50 rows of archive completeness records, grouped by date from 2026-09-01 to 2026-10-20, with the field set to expected_items? I want to spot which dates had the biggest gaps between what we indexed and what we expected.”
Channel group normalisation comparison
“Run string normalisation and a weighted mean for me on these archive completeness rows — I have about 120 records across three channel groups with both indexed_items and expected_items filled in, and I want the analysis run against indexed_items so the two reports can be compared side by side.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-news-23-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local, offline-style computation of string normalisation and weighted mean statistics on news archive completeness records without any live data lookup or external enrichment. Ideal for batch audit workflows analysing up to 200 rows of channel-group-segmented archive data where two paired analytical reports are needed per run.
Side effects
- Missing required fields (row_id, date, channel_group) return validation errors
- Supplying more than 200 rows exceeds the maximum and will be rejected
- Invalid date format (not YYYY-MM-DD) causes a schema validation failure
- Null values for both indexed_items and expected_items may produce undefined weighted mean output
- Invalid field enum value (not 'indexed_items' or 'expected_items') returns a bad request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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