# Feature Availability Analytics — Grouped Numeric Summary + Missing-Value Fill

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

Computes a grouped numeric summary and fills missing values for feature availability records (populated_features, expected_features) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-099?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-grouped-numeric-summary-missing-value-c9bf9b88
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_RQNZbjLKpf_FmsZZHUu5J

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-grouped-numeric-summary-missing-value-c9bf9b88 -d '<json body>'
```

Example prompt: Summarize my feature availability records by dataset group and fill any missing populated_features values using the median — here are my rows: row-1 (group-a, 2026-10-01, populated=10, expected=1), row-2 (group-b, 2026-10-02, populated=20, expected=2), row-3 (group-a, 2026-10-03, populated=15, expected=3).

## When to prefer this

Choose this endpoint when you need lightweight, local grouped statistics and null imputation for feature availability records without any external lookups or predictions. Ideal for data pipeline health checks, ML feature store auditing, or quick dataset completeness diagnostics across up to 200 rows at minimal cost ($0.01 USDC for 2 reports).

## Known failure modes

- More than 200 rows submitted — request rejected, only up to 200 rows accepted
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) causes request rejection
- Invalid fill method or field enum value causes schema error
- Payment of 0.01 USDC not provided or incorrect — x402 payment required

## How this service works

Feature availability — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + Explicit missing-value fill 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 grouped numeric summary breaking down feature availability metrics (populated_features or expected_features) by dataset_group, and (2) an explicit missing-value fill report where nulls in the selected field are replaced using zero, mean, or median imputation. Both reports are computed locally with no external data fetching.

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-grouped-numeric-summary-missing-value-c9bf9b88/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)