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Brand Mention Inventories — Equal-Width Histogram + IQR Outlier Flags
x402
Brand Mention Inventories — Equal-Width Histogram + IQR Outlier Flags 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).
Generates an equal-width histogram and IQR-based outlier flags for brand mention inventory records (mention_count or source_count) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Equal-width histogram + IQR outlier flags: Equal-width histogram + IQR outlier flags 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
Returns two analytical reports for the supplied dataset: (1) an equal-width histogram showing bin boundaries and row counts for the selected field (mention_count or source_count), and (2) IQR-based outlier flags identifying which rows fall outside the interquartile fence, with row IDs and values. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Detecting Mention Spikes Across Brands
“I have 80 rows of brand mention data for three company groups from last quarter — can you run an equal-width histogram with 8 bins on the mention_count field and flag any IQR outliers so I know which dates had unusual spike activity?”
Source Diversity Distribution Check
“Can you analyze the distribution of source_count values in my brand mention inventory — I have 120 records across two company groups — using 5 bins and flag any outliers so I can see if certain records have abnormally high or low source diversity?”
Monthly Mention Inventory QA Report
“I need a statistical QA report on this month's brand mention inventory — 200 rows, mention_count field, 6 bins please — flag anything that looks like an outlier using IQR so my team can investigate before we finalize the report.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-170?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local statistical analysis of brand mention inventory data specifically — particularly when you want both a histogram view of distribution and IQR-based outlier detection in a single call for up to 200 rows. It is ideal for data QA pipelines, periodic brand monitoring audits, and anomaly detection workflows where you already have structured mention/source count data. Prefer alternatives if you need live data fetching, prediction, or analysis of more than 200 rows.
Side effects
- More than 200 rows submitted — request rejected, batch must be split
- Missing required row fields (row_id, company_group, date, mention_count, source_count) — validation error
- bins parameter outside 1–30 range — rejected
- Null values in the selected numeric field may affect histogram bin boundaries
- Date field not matching YYYY-MM-DD pattern — row rejected
- All values identical — histogram collapses to single bin, IQR is zero causing all-or-none outlier flags
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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