# Brand Mention Inventories — Numeric Distribution + IQR Outlier Flags

> Brand Mention Inventories — Numeric Distribution + IQR Outlier Flags 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 numeric distribution statistics and IQR-based outlier flags for brand mention inventory data (mention_count and source_count fields) across up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-080?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-numeric-distribution-iqr-outlier-flags-40439440
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Hx_MTlrIgiDkHte_3T7t_

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-numeric-distribution-iqr-outlier-flags-40439440 -d '<json body>'
```

Example prompt: Can you run a numeric distribution and IQR outlier analysis on my brand mention inventory data? I have 50 rows with row_id, company_group, date, mention_count, and source_count — please analyze the mention_count field and flag any outliers.

## When to prefer this

Choose this endpoint when you have a pre-collected dataset of brand mention inventory records (up to 200 rows) and need fast, local statistical analysis — specifically numeric distribution summaries and IQR-based outlier detection — without requiring live data lookup, external APIs, or ML predictions. Ideal for data quality audits, anomaly detection in brand tracking pipelines, or exploratory analysis of mention and source count distributions across company groups.

## Known failure modes

- Input exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, company_group, date, mention_count, source_count) cause validation errors
- Invalid date format (must match YYYY-MM-DD pattern) triggers schema rejection
- Null values in the selected numeric field may affect distribution calculations
- Field enum value not matching 'mention_count' or 'source_count' causes parameter error
- Empty rows array (minItems: 1 not met) returns an error

## How this service works

Brand mention inventories — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags 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 for up to 200 rows: (1) a numeric distribution summary (e.g. min, max, mean, median, quartiles) for the selected field (mention_count or source_count), and (2) IQR-based outlier flags identifying which rows fall outside the expected range. No external data is fetched; all analysis is performed on the supplied 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-numeric-distribution-iqr-outlier-flags-40439440/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)