# Brand Mention Inventories — Category Frequencies + Date by Category Pivot

> Brand Mention Inventories — Category Frequencies + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Analyzes brand mention inventory records to produce two reports: category frequency counts and a date-by-category pivot, for up to 200 rows of mention and source count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-110?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/brand-mention-inventories-category-frequencies-date-by-category-pivot-26921834
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wLg_MWfP6BzLIGk0uKwb0

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability brand-mention-inventories-category-frequencies-date-by-category-pivot-26921834 -d '<json body>'
```

Example prompt: I have a list of brand mention records with company groups, dates, mention counts, and source counts — can you run a category frequency breakdown and a date-by-category pivot on them, analyzing the mention_count field, for up to 200 rows?

## When to prefer this

Choose this endpoint when you have up to 200 pre-collected brand mention records with company group and date dimensions and need both a category frequency breakdown and a date-by-category pivot computed analytically — without requiring live data lookup, external enrichment, or predictions. It is ideal for lightweight, low-cost ($0.01 USDC) offline batch summarization of brand mention inventory data already in hand.

## Known failure modes

- Exceeding 200 rows causes rejection — trim input to at most 200 records
- Missing required fields (row_id, company_group, date, mention_count, source_count) on any row causes validation failure
- Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation
- Providing an invalid field enum value (not 'mention_count' or 'source_count') returns an error
- Submitting an empty rows array (minItems:1 violated) returns a validation error

## How this service works

Brand mention inventories — Category frequencies + Date by category pivot: Category frequencies + Date by category pivot 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 analytical reports computed locally from the supplied data: (1) a category frequency table showing aggregated mention_count or source_count totals per company_group, and (2) a date-by-category pivot table cross-tabulating the selected metric (mention_count or source_count) across dates and company groups. No external data is fetched.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "company_group",
     "date",
     "mention_count",
     "source_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "company_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "mention_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_count": 1,
     "company_group": "group-a",
     "mention_count": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_count": 2,
     "company_group": "group-b",
     "mention_count": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_count": 3,
     "company_group": "group-a",
     "mention_count": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_count": 4,
     "company_group": "group-b",
     "mention_count": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "mention_count",
    "source_count"
   ],
   "type": "string",
   "default": "mention_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/brand-mention-inventories-category-frequencies-date-by-category-pivot-26921834/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)