# Feature Availability Analytics — String Normalisation + Numeric Distribution

> Feature Availability Analytics — String Normalisation + 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 two local analytical reports (string normalisation and numeric distribution) on up to 200 rows of feature availability data (populated_features, expected_features).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-022?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-string-normalisation-numeric-ef163ff3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wNFPgESpWWVubRueSKMma

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-string-normalisation-numeric-ef163ff3 -d '<json body>'
```

Example prompt: Can you run string normalisation and numeric distribution reports on my feature availability data? I have up to 50 rows with row_id, dataset_group, date, populated_features, and expected_features fields — analyze the populated_features column.

## When to prefer this

Choose this endpoint when you need lightweight, local statistical profiling of feature availability data — specifically both string normalisation and numeric distribution in a single call — without requiring external data lookups, predictions, or professional assessments. Ideal for data pipeline audits, ML feature completeness checks, and dataset quality reviews on batches of up to 200 rows at very low cost ($0.01 USDC for both reports).

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Invalid field enum value (not 'populated_features' or 'expected_features') fails validation

## How this service works

Feature availability — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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 string normalisation report summarising text-form values in the selected feature column, and (2) a numeric distribution report with statistical summaries (e.g. min, max, mean, percentiles) of the numeric feature values — all computed locally against the supplied rows 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-string-normalisation-numeric-ef163ff3/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)