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Brand Mention Inventories — Adjacent Row Changes + Base-100 Series
x402
Brand Mention Inventories — Adjacent Row Changes + Base-100 Series 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).
Computes adjacent row-to-row changes and a Base-100 index series for brand mention inventory data (mention_count, source_count) supplied by the caller, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Adjacent row changes + Base-100 series: Adjacent row changes + Base-100 series 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 derived from the caller-supplied rows: (1) an adjacent row changes report showing period-over-period differences in the selected field (mention_count or source_count), and (2) a Base-100 index series anchored at the specified base_index row — all computed locally without any external lookup or live data fetch, for up to 200 rows.
Example use cases
Brand mention trend indexing
“I have 30 rows of daily brand mention data for three company groups across Q3. Can you run the adjacent row changes and Base-100 series reports on the mention_count field, using row index 0 as the base?”
Source diversity shift analysis
“Here are 60 rows of brand monitoring records — I want to see how source_count shifts between consecutive rows and an indexed series starting from the first entry so I can spot when our media coverage diversified.”
Competitive mention benchmarking
“I've got weekly mention_count snapshots for group-a, group-b, and group-c — 90 rows total. Run the adjacent changes and base-100 index reports on mention_count with base index at row 0 so I can compare momentum across groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-155?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you already have brand mention inventory data in hand and need two specific analytical outputs — adjacent row changes and a Base-100 normalized index series — computed on that supplied data without any external lookup. It is ideal for time-series brand monitoring workflows where you want to quantify period-over-period momentum and normalize trends to a common baseline. Prefer it over raw computation when you need both reports in a single $0.01 call for up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required row fields (row_id, date, company_group, mention_count, source_count) — validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- base_index out of range (>199 or <0) — rejected by schema
- No rows submitted (minItems:1 violated) — empty array error
- Non-numeric values for mention_count or source_count where numbers are expected — type error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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