# Feature Availability Analytics — Numeric Distribution + Date by Category Pivot

> Feature Availability Analytics — Numeric Distribution + 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 2026-10-19).

Runs two local analytical reports on supplied feature availability data: a numeric distribution summary and a date-by-category pivot, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-083?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-numeric-distribution-date-by-category-07a1a89e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Fv9oLBtwgKASEZ8WefYnX

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-distribution-date-by-category-07a1a89e -d '<json body>'
```

Example prompt: I have 4 rows of feature availability data — group-a on 2026-10-01 with 10 populated and 1 expected, group-b on 2026-10-02 with 20 populated and 2 expected, group-a on 2026-10-03 with 15 populated and 3 expected, and group-b on 2026-10-04 with 30 populated and 4 expected — can you run a numeric distribution and date-by-category pivot on the populated_features field?

## When to prefer this

Choose this endpoint when you need fast, offline statistical analysis of feature availability records — specifically a numeric distribution plus a date-by-category pivot — without any external API calls, predictions, or live data lookups. Ideal for auditing ML dataset completeness, monitoring data pipeline feature coverage, or reviewing feature store population rates across groups over time. Best for batches of up to 200 rows where both reports are needed simultaneously at low cost.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (-1T to 1T) — rejected
- Invalid field enum value (not populated_features or expected_features) — error

## How this service works

Feature availability — Numeric distribution + Date by category pivot: Numeric distribution + 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

Two independent analytical reports: (1) a numeric distribution of the selected field (populated_features or expected_features) showing statistical spread across the supplied rows, and (2) a date-by-category pivot table breaking down values by dataset_group and date. Both reports are computed locally from the submitted data with no external lookups.

## 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-distribution-date-by-category-07a1a89e/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)