# Feature Availability — String Normalisation + Adjacent Row Changes

> Feature Availability — String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).

Analyzes feature availability records by performing string normalisation and detecting adjacent row changes across up to 200 rows, producing 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-029?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-string-normalisation-adjacent-row-changes-f2a17721
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_UAflOm0iILJZaxJJXAb5c

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-string-normalisation-adjacent-row-changes-f2a17721 -d '<json body>'
```

Example prompt: Can you run string normalisation and adjacent row change analysis on my feature availability data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04 with populated_features values like 10, 20, 15, 30 and expected_features of 1, 2, 3, 4 — analyze the populated_features field.

## When to prefer this

Choose this endpoint when you need fast, local (no external lookup) analytical computation on feature availability records — specifically when you need both string normalisation and adjacent row change detection in a single call for up to 200 rows. Ideal for ML pipeline data quality checks, feature store audits, and dataset completeness monitoring where you want two distinct reports from one low-cost API call.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Null values in both populated_features and expected_features — may produce incomplete report
- Invalid field enum value (must be 'populated_features' or 'expected_features') — error
- Empty rows array — minimum 1 item required

## How this service works

Feature availability — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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 standardizing the feature availability values across submitted rows, and (2) an adjacent row changes report identifying where consecutive rows differ in the selected feature field (populated_features or expected_features). Results cover up to 200 submitted rows with no external data sources.

## 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-string-normalisation-adjacent-row-changes-f2a17721/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)