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Brand Mention Inventories — Record Quality Audit + Two-Measure Correlation
x402
Brand Mention Inventories — Record Quality Audit + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Audits data quality and computes Pearson/Spearman correlation between mention_count and source_count for up to 200 brand mention inventory rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + Two-measure correlation: Record quality audit + Two-measure correlation for supplied brand mention inventories records (mention_count, source_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
Two independent analytical reports: (1) a record quality audit flagging missing values, out-of-range entries, and data completeness issues across all submitted rows; (2) a two-measure correlation report computing the statistical relationship between mention_count and source_count across the dataset. No external lookups or predictions are made — all analysis is performed locally on the supplied data.
Example use cases
Brand data quality check before reporting
“Before I send this brand mention report to my client, can you audit these 150 rows of mention and source count data for group-a and group-b — flag any bad records and tell me if mention_count and source_count are correlated?”
Comparing two brands' mention tracking consistency
“I have brand mention inventory data for two company groups covering Q3 2026, about 80 rows total. Can you run a record quality audit and tell me how correlated the mention counts are with the source counts across both groups?”
Validating scraped brand mention data pipeline
“We just scraped a new batch of brand mentions — 200 rows with mention_count and source_count per day per group. Can you check the data quality and compute the correlation between those two measures so I know if the pipeline is working correctly?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-015?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular brand mention inventory data (up to 200 rows) with mention_count and source_count fields and need both a data quality audit and a statistical correlation between those two measures in a single call. It is ideal for pre-reporting validation, pipeline QA, or exploratory analysis of brand monitoring datasets. It does not perform live lookups, predictions, or external enrichment — prefer it when you already have the data and need offline analytical summaries.
Side effects
- Fewer than 1 row submitted — request rejected
- More than 200 rows submitted — exceeds row limit
- Missing required fields (row_id, company_group, date, mention_count, source_count) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — rejected
- All values null for a measure — correlation cannot be computed
- Payment of 0.01 USDC not provided — request blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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