# Feature Availability Analytics — Record Quality Audit, String Normalisation & Numeric Row Ordering

> Feature Availability Analytics — Record Quality Audit, String Normalisation & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Runs three local analytical reports on feature availability records: a record quality audit, string normalisation, and numeric row ordering — up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-196?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-record-quality-audit-string-c26dd55a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_t6CoRV202OMGMRxmLYCay

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-string-c26dd55a -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and numeric row ordering on my feature availability data? I have up to 50 rows with populated_features and expected_features values across multiple dataset groups — use populated_features as the field to order.

## When to prefer this

Choose this endpoint when you need three bundled local analytics reports (quality audit, string normalisation, numeric ordering) on feature availability data in a single cheap call ($0.01 USDC). Ideal for ML/data ops teams validating feature datasets before ingestion or downstream use. Prefer this over individual analytics tools when you want all three report types in one round trip without external API calls or live lookups.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern mismatch error
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — validation failure
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — schema error
- Invalid field enum value (must be 'populated_features' or 'expected_features') — enum validation error
- Payment not attached or insufficient USDC — x402 payment required error

## How this service works

Feature availability — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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 analytical reports for the submitted rows: (1) a record quality audit flagging data issues, (2) a string normalisation report standardising text fields, and (3) a numeric row ordering report ranking rows by the selected numeric field (populated_features or expected_features). All computed 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-string-c26dd55a/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)