# Feature Availability Analytics: Record Quality Audit + String Normalisation + Category Frequencies

> Feature Availability Analytics: Record Quality Audit + String Normalisation + Category Frequencies 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 analytical reports (record quality audit, string normalisation, and category frequencies) on up to 200 feature availability records containing populated and expected feature counts.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-195?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-string-45a81687
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_igrod_8k2cDbsS0VCQw02

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-45a81687 -d '<json body>'
```

Example prompt: Can you run a full data quality audit, string normalisation, and category frequency analysis on my feature availability records? I have 4 rows across two dataset groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04, and I want all three reports in one shot.

## When to prefer this

Choose this endpoint when you need three data quality reports (quality audit, string normalisation, category frequencies) on feature availability records in a single cost-efficient call. Ideal when working with ML feature stores or data pipelines where you want to check feature completeness and group distributions offline, without external lookups or predictions. Best suited for batches of 1–200 rows where all three report types are needed together.

## Known failure modes

- Fewer than 1 row submitted — minimum row count not met
- More than 200 rows submitted — exceeds batch limit
- Missing required fields (row_id, date, populated_features, expected_features) in one or more rows
- Date field does not match YYYY-MM-DD pattern — validation error
- populated_features or expected_features values outside allowed numeric range
- row_id exceeds 80 character limit
- Payment of 0.01 USDC not completed — request rejected

## How this service works

Feature availability — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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

Returns three independent analytical reports for the supplied feature availability rows: (1) a record quality audit flagging completeness and consistency issues, (2) string normalisation results for text fields like dataset_group, and (3) category frequency counts summarising distribution across groups and dates — 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-record-quality-audit-string-45a81687/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)