# Brand Mention Inventories — Record Quality Audit + Explicit Missing-Value Fill

> Brand Mention Inventories — Record Quality Audit + Explicit Missing-Value Fill 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).

Audits record quality and fills missing values (mention_count or source_count) in brand mention inventory datasets using local analytical engines, producing two independent reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-019?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-record-quality-audit-explicit-missing-value-119de403
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_b5o15daFyEpvktHo7lQ0B

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-explicit-missing-value-119de403 -d '<json body>'
```

Example prompt: Can you audit these brand mention records for quality issues and fill in any missing mention_count values using median imputation? I have up to 50 rows with fields like row_id, company_group, date, mention_count, and source_count.

## When to prefer this

Choose this endpoint when you have brand mention inventory data (with mention_count and source_count fields) that needs both a quality audit and missing-value imputation in a single call. It is ideal for preprocessing brand monitoring exports before analytics, dashboards, or reporting pipelines. Prefer it over generic data cleaning tools when your data fits the brand mention schema and you need two structured reports simultaneously for up to 200 rows at very low cost.

## Known failure modes

- Exceeding 200 rows returns an error — batch must be split
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Numeric values outside allowed range trigger validation errors
- Empty rows array rejected (minimum 1 row required)
- Unsupported method or field enum value causes request failure
- Missing required fields (row_id, company_group, date) cause schema validation errors

## How this service works

Brand mention inventories — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill 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 independent reports: (1) a record quality audit identifying issues such as nulls, invalid formats, or anomalies across the supplied brand mention rows; and (2) an explicit missing-value fill report showing which cells were imputed and what values were assigned using the chosen method (zero, mean, or median) for the specified field (mention_count or source_count).

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/brand-mention-inventories-record-quality-audit-explicit-missing-value-119de403/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)