# Feature Availability Analytics: Numeric Row Ordering + Date by Category Pivot

> Feature Availability Analytics: Numeric Row Ordering + Date by Category Pivot 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).

Generates two local analytical reports (numeric row ordering and date-by-category pivot) for supplied feature availability records with populated_features and expected_features fields, processing up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-122?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/feature-availability-analytics-numeric-row-ordering-date-by-category-c38ec733
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_A31EIAPHjcvxxvzAyjn4n

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 feature-availability-analytics-numeric-row-ordering-date-by-category-c38ec733 -d '<json body>'
```

Example prompt: I have feature availability records for two groups — group-a and group-b — with dates ranging from 2026-10-01 to 2026-10-04 and populated_features counts like 10, 20, 15, 30 against expected_features of 1, 2, 3, 4. Can you run the numeric row ordering and date-by-category pivot reports on populated_features for all 4 rows?

## When to prefer this

Choose this endpoint when you have feature availability tabular data (with populated vs expected feature counts, dates, and group labels) and need both a ranked row ordering and a date-by-category pivot in a single low-cost call. It is ideal for offline/batch analytical summaries of up to 200 records where no live lookup, prediction, or external enrichment is needed. At $0.01 for two reports, it is highly cost-effective for recurring dataset quality checks.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes schema rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Null values in both numeric fields may produce incomplete pivot cells
- Empty rows array (minItems:1 violated) returns an error

## How this service works

Feature availability — Numeric row ordering + Date by category pivot: Numeric row ordering + Date by category pivot for supplied feature availability records (populated_features, expected_features). 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 derived from the supplied row data: (1) a numeric row ordering ranking rows by the selected field (populated_features or expected_features), and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group. No external data is fetched; all computation is performed locally on the submitted rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "populated_features",
     "expected_features"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "expected_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "populated_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_features": 1,
     "populated_features": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_features": 2,
     "populated_features": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_features": 3,
     "populated_features": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_features": 4,
     "populated_features": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "populated_features",
    "expected_features"
   ],
   "type": "string",
   "default": "populated_features"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-numeric-row-ordering-date-by-category-c38ec733/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)