# Brand Mention Inventories — Base-100 Series + Explicit Missing-Value Fill

> Brand Mention Inventories — Base-100 Series + 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 2026-10-20).

Computes a base-100 indexed time series and fills missing values (zero, mean, or median) for brand mention inventory data (mention_count, source_count) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-169?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-base-100-series-explicit-missing-value-fill-4ab9a67b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Y_LTxDZW-ToZPdEKzjl3g

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-base-100-series-explicit-missing-value-fill-4ab9a67b -d '<json body>'
```

Example prompt: I have a brand mention dataset with up to 200 rows for two company groups across dates in October 2026 — can you rebase the mention_count field to a base-100 index starting at row 0, and fill any missing values using the median, for both groups?

## When to prefer this

Choose this endpoint when you have pre-collected brand mention inventory data (mention_count or source_count) organized by date and company group, and need both a base-100 normalized time series and explicit missing-value imputation in a single call. Ideal for offline/batch data cleaning and normalization workflows where no live data fetch, prediction, or external enrichment is needed. Best suited for datasets up to 200 rows; for larger datasets, split into multiple calls.

## Known failure modes

- More than 200 rows supplied — request exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- base_index out of range (0–199) relative to actual row count
- All values for the target field are null, making mean/median fill undefined
- row_id missing or empty string causes required field validation error
- Numeric values outside ±1,000,000,000,000 bounds rejected

## How this service works

Brand mention inventories — Base-100 series + Explicit missing-value fill: Base-100 series + 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 analytical reports: (1) a base-100 indexed series for the specified field (mention_count or source_count) relative to the chosen baseline row, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports cover up to 200 input rows and are computed locally without 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
  },
  "field": {
   "enum": [
    "mention_count",
    "source_count"
   ],
   "type": "string",
   "default": "mention_count"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/brand-mention-inventories-base-100-series-explicit-missing-value-fill-4ab9a67b/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)