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Brand Mention Inventories — String Normalisation + Secondary-Measure Weighted Mean
x402
Brand Mention Inventories — 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 2026-10-16).
Normalises brand mention inventory strings and computes a source-count-weighted mean for mention_count or source_count across up to 200 rows, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean 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 2 independent analytical reports: one covering string normalisation of company_group and date fields, and one providing a secondary-measure weighted mean (e.g. mention_count weighted by source_count or vice versa) aggregated across the supplied rows. No external data is fetched; all computation is local to the supplied dataset of up to 200 rows.
Example use cases
Weekly brand mention summary by group
“I've got 80 rows of brand mention data for three company groups over the last month — each row has a row_id, company_group, date, mention_count, and source_count. Can you normalize the strings and give me the source-count weighted mean for mention_count across all of them?”
Comparing source reach across brand groups
“I want to understand how source_count is distributed across my brand monitoring data — here are 120 rows covering group-a and group-b. Please normalize the entries and compute the weighted mean using mention_count as the weight, targeting source_count as the field.”
Data quality pass before brand reporting
“Before I put together our monthly brand report, can you run a string normalisation and weighted mean calculation on these 60 brand mention inventory rows? Use mention_count as the field and source_count as the secondary measure weight — I need the two reports this endpoint provides.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have brand mention inventory tabular data (up to 200 rows) and need both string normalisation of group/date fields and a secondary-measure weighted mean statistic in a single low-cost call ($0.01 USDC for 2 reports). Prefer it over generic analytics tools when working specifically with mention_count and source_count brand monitoring fields and you want local analytical computation without any live lookup or external data dependency.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, company_group, date, mention_count, source_count) cause validation errors
- Null values in the weight field (source_count) may cause division-by-zero or skewed weighted mean
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Empty rows array rejected (minItems: 1)
- Numeric values outside ±1,000,000,000,000 boundary cause schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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