# Feature Availability Analytics — Record Quality Audit, String Normalisation & Secondary-Measure Weighted Mean

> Feature Availability Analytics — Record Quality Audit, String Normalisation & Secondary-Measure Weighted Mean 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 bundled analytical reports (record quality audit, string normalisation, and secondary-measure weighted mean) 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-10-205?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-1a5f7390
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_SboR71MjDT83oftWAHJiI

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-1a5f7390 -d '<json body>'
```

Example prompt: Can you run a quality audit, string normalisation, and weighted mean analysis on my feature availability records? I have 4 rows: row-1 on 2026-10-01 in group-a with 10 populated and 1 expected, row-2 on 2026-10-02 in group-b with 20 populated and 2 expected, row-3 on 2026-10-03 in group-a with 15 populated and 3 expected, and row-4 on 2026-10-04 in group-b with 30 populated and 4 expected — use populated_features as the primary field.

## When to prefer this

Choose this endpoint when you need three co-located data quality operations (audit, normalisation, weighted mean) on feature availability records in a single low-cost call, avoiding the overhead of three separate API calls. Ideal for data engineering and MLOps workflows where feature completeness tracking is batched up to 200 rows and external lookups or predictions are not needed.

## Known failure modes

- Rows array empty or missing — validation error returned
- More than 200 rows submitted — rejected with max-items error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- populated_features or expected_features out of allowed numeric range — rejected
- field enum value not 'populated_features' or 'expected_features' — schema validation error
- row_id missing or empty string — required field validation failure

## How this service works

Feature availability — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean 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 submitted feature availability rows: (1) a record quality audit identifying issues such as nulls, out-of-range values, and structural anomalies; (2) string normalisation results for relevant text fields; and (3) a secondary-measure weighted mean computed over the selected numeric field (populated_features or expected_features), all processed locally without 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-1a5f7390/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)