# Brand Mention Inventories — Record Quality Audit, String Normalisation & Category Frequencies

> Brand Mention Inventories — Record Quality Audit, String Normalisation & Category Frequencies 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).

Runs three local analytical reports on brand mention inventory rows: record quality audit, string normalisation, and category frequency analysis, for up to 200 rows of mention_count/source_count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-195?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-record-quality-audit-string-normalisation-2b0ff0c5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LuJArfMvmRiQgjmtkTyDr

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-record-quality-audit-string-normalisation-2b0ff0c5 -d '<json body>'
```

Example prompt: Can you audit, normalise, and get category frequencies for these brand mention records? I have up to 200 rows with fields row_id, company_group, date, mention_count, and source_count — I need all three reports: record quality audit, string normalisation, and category frequencies.

## When to prefer this

Choose this endpoint when you need quick, offline data quality and normalisation analysis for brand mention inventory datasets of up to 200 rows, without requiring external API calls, live data lookups, or predictive modelling. It is ideal for batch data cleaning and auditing workflows where you want three complementary analytical perspectives (quality, normalisation, frequencies) in a single cheap call.

## Known failure modes

- Submitting more than 200 rows will be rejected or truncated
- Missing required fields (row_id, company_group, date, mention_count, source_count) will cause row-level errors
- Dates not matching YYYY-MM-DD pattern will fail schema validation
- Numeric fields (mention_count, source_count) outside the allowed range will be rejected
- Empty rows array will fail minItems validation

## How this service works

Brand mention inventories — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied brand mention inventories records (mention_count, source_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three structured analytical reports for the supplied rows: (1) a record quality audit flagging anomalies, missing values, and out-of-range fields; (2) a string normalisation report with cleaned and standardised company_group and other string fields; (3) a category frequency table showing counts and distributions by company_group and date categories. All computed locally with no external lookups.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/brand-mention-inventories-record-quality-audit-string-normalisation-2b0ff0c5/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)