# Feature Availability Analytics — String Normalisation + Grouped Numeric Summary

> Feature Availability Analytics — String Normalisation + Grouped Numeric Summary 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).

Produces two analytical reports — string normalisation and grouped numeric summary — for supplied feature availability records (populated_features, expected_features) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-023?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-grouped-numeric-fdf92712
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ji2T_ILpMd1SuOFmTY8xy

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-grouped-numeric-fdf92712 -d '<json body>'
```

Example prompt: Run a string normalisation and grouped numeric summary on my feature availability data — I have 4 rows across group-a and group-b dated 2026-10-01 through 2026-10-04, with populated_features values of 10, 20, 15, 30 against expected_features of 1, 2, 3, 4 respectively, and I want the analysis on the populated_features field.

## When to prefer this

Choose this endpoint when you need simultaneous string normalisation and grouped numeric summary analytics on feature availability records (populated vs expected features) without any external data enrichment, live lookup, or model inference. Ideal for data quality audits, pipeline monitoring, and feature completeness reporting on batches up to 200 rows at low cost ($0.01 USDC for both reports).

## Known failure modes

- More than 200 rows supplied — request will be rejected (maxItems constraint)
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Null values in numeric fields may affect summary statistics
- Invalid field selector (not 'populated_features' or 'expected_features') — enum validation error

## How this service works

Feature availability — String normalisation + Grouped numeric summary: String normalisation + Grouped numeric summary 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

Returns two independent analytical reports: (1) a string normalisation report over the selected field values across all supplied rows, and (2) a grouped numeric summary breaking down statistics by dataset_group. Both reports are generated locally from the supplied data with no external lookups or predictions.

## 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-grouped-numeric-fdf92712/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)