# Feature Availability Analytics: Numeric Row Ordering + Two-Measure Correlation

> Feature Availability Analytics: Numeric Row Ordering + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Runs two local analytical reports — numeric row ordering and two-measure correlation — on up to 200 feature availability records (populated_features vs expected_features) per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-120?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-numeric-row-ordering-two-measure-e7d486e5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5FvR1vXD7keC9XMu_IrPd

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-numeric-row-ordering-two-measure-e7d486e5 -d '<json body>'
```

Example prompt: Can you run numeric row ordering and two-measure correlation on my feature availability data? I have rows for group-a and group-b from October 2026, each with populated_features and expected_features counts — sort and correlate on populated_features.

## When to prefer this

Choose this endpoint when you need quick, offline analytical computation — specifically numeric ordering and cross-measure correlation — on feature availability data without any external lookup, prediction, or human assessment. It is ideal for data quality audits, ML pipeline completeness checks, and dataset group comparisons where you already have both populated and expected feature counts and want statistical insight at minimal cost ($0.01 USDC for two reports).

## Known failure modes

- Missing required fields (row_id, date, populated_features, expected_features) causes validation error
- Date strings not matching YYYY-MM-DD pattern are rejected
- More than 200 rows submitted exceeds the per-call limit
- Null values in both measure fields for a row may produce incomplete correlation results
- row_id exceeding 80 characters is rejected
- Submitting zero rows violates minItems constraint

## How this service works

Feature availability — Numeric row ordering + Two-measure correlation: Numeric row ordering + Two-measure correlation 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 analytical reports: (1) a numeric row ordering of the submitted rows ranked by the selected field (populated_features or expected_features), and (2) a two-measure correlation report quantifying the statistical relationship between populated_features and expected_features across the submitted rows. No external data is fetched; all analysis is performed locally on the supplied rows (up to 200).

## 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-numeric-row-ordering-two-measure-e7d486e5/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)