# Brand Mention Inventories — String Normalisation + Secondary-Measure Weighted Mean

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-034?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/brand-mention-inventories-string-normalisation-secondary-measure-fddb1d6d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XaCrJLYt9Jcd4hewKubPj

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-string-normalisation-secondary-measure-fddb1d6d -d '<json body>'
```

Example prompt: I have brand mention inventory records for two company groups over the past week — can you normalize the strings and calculate the source-count weighted mean for mention_count? Here are up to 50 rows with row_id, company_group, date, mention_count, and source_count fields.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## 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-string-normalisation-secondary-measure-fddb1d6d/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)