# Feature Availability — Record Quality Audit + Numeric Distribution

> Feature Availability — Record Quality Audit + Numeric Distribution 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 (record quality audit and numeric distribution summary) on up to 200 feature availability records containing populated_features and expected_features fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-004?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-record-quality-audit-numeric-distribution-e33e0a74
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ucThYxQ3Tgb43FTLtVVqK

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-record-quality-audit-numeric-distribution-e33e0a74 -d '<json body>'
```

Example prompt: Can you run a record quality audit and numeric distribution report on my feature availability data — here are 50 rows with row_id, dataset_group, date, populated_features, and expected_features — and analyze the populated_features field?

## When to prefer this

Choose this endpoint when you need to validate and understand the numeric distribution of feature availability data (populated vs expected features) for up to 200 rows in a single low-cost call. It is ideal for data quality pipelines, ML feature store audits, and dataset completeness checks where you want both a quality audit and a distribution summary together without any external lookups or live data dependencies. Prefer it over general-purpose analytics when your data fits the specific feature availability schema (populated_features, expected_features, dataset_group, date).

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is not accepted (minItems: 1)
- Invalid field enum value other than 'populated_features' or 'expected_features' causes error

## How this service works

Feature availability — Record quality audit + Numeric distribution: Record quality audit + Numeric distribution 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 independent analytical reports: (1) a record quality audit flagging issues such as null values, out-of-range values, or malformed entries across the supplied rows, and (2) a numeric distribution summary including quantiles, min, max, mean, and spread statistics for the selected field (populated_features or expected_features), covering up to 200 rows processed entirely with local analytical engines.

## 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-record-quality-audit-numeric-distribution-e33e0a74/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)