# Feature Availability — Record Quality Audit + String Normalisation

> Feature Availability — Record Quality Audit + String Normalisation 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).

Audits data quality and normalises strings for up to 200 feature availability records, returning two independent analytical reports with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-001?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-string-normalisation-c2e6825e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7sRHJSuiMc3RX1wTOmFD1

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-string-normalisation-c2e6825e -d '<json body>'
```

Example prompt: Can you run a record quality audit and string normalisation on my feature availability data? I have 4 rows — row-1 for group-a on 2026-10-01 with 10 populated and 1 expected, row-2 for group-b on 2026-10-02 with 20 populated and 2 expected, row-3 for group-a on 2026-10-03 with 15 populated and 3 expected, and row-4 for group-b on 2026-10-04 with 30 populated and 4 expected.

## When to prefer this

Choose this endpoint when you need both a record quality audit and string normalisation on feature availability data in a single low-cost call (0.01 USDC for both reports). Ideal for ML pipelines validating feature completeness before training, data engineering teams auditing feature stores, or anyone needing offline analytical checks on populated vs expected feature counts without any live lookups or external API dependencies.

## Known failure modes

- Exceeds 200-row limit — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Date not in YYYY-MM-DD format — pattern validation failure
- populated_features or expected_features outside allowed numeric range — schema rejection
- Empty rows array — minItems constraint violation

## How this service works

Feature availability — Record quality audit + String normalisation: Record quality audit + String normalisation 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 record quality audit highlighting data issues across the supplied feature availability rows, and (2) a string normalisation report cleaning and standardising string fields. Both reports are generated locally with no external lookups, covering up to 200 rows in a single call.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-record-quality-audit-string-normalisation-c2e6825e/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)