# Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication, and Numeric Distribution

> Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication, and 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 three local analytical reports on feature availability data: a record quality audit, exact row deduplication, and numeric distribution analysis for populated_features or expected_features.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-210?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-record-quality-audit-exact-row-0647bc11
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_4DAAAdu82TKTKsa3p4z4Z

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-record-quality-audit-exact-row-0647bc11 -d '<json body>'
```

Example prompt: Can you run a full quality audit, exact duplicate check, and numeric distribution report on my feature availability records — here are up to 200 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 three complementary data quality reports — quality audit, deduplication, and numeric distribution — on feature availability records in a single low-cost call. Ideal when data is already in hand (no live lookup needed), batch size is under 200 rows, and you want offline, deterministic analytics without external dependencies. Prefer over single-report endpoints when you need all three analyses simultaneously for the same dataset.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) causes row-level rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside the allowed range trigger input rejection
- Empty rows array returns an error
- Payment not included or insufficient USDC causes 402 response

## How this service works

Feature availability — Record quality audit + Exact row deduplication + Numeric distribution: Record quality audit + Exact row deduplication + Numeric distribution for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent reports: (1) a record quality audit flagging null, invalid, or malformed values in the submitted feature availability rows; (2) an exact row deduplication report identifying any fully duplicate rows; (3) a numeric distribution analysis (e.g. min, max, mean, percentiles, histogram) for either the populated_features or expected_features column as specified. All processing is done locally 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-record-quality-audit-exact-row-0647bc11/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)