# Feature Availability Analytics: Numeric Interval Filter + Two-Measure Correlation

> Feature Availability Analytics: Numeric Interval Filter + 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 analytical reports on feature availability data — a numeric interval filter on populated_features or expected_features, plus a Pearson/correlation analysis between the two measures — for up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-131?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-interval-filter-two-measure-ccfd6692
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_f4sVcwai_NWio5QyQAztc

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-interval-filter-two-measure-ccfd6692 -d '<json body>'
```

Example prompt: I have 150 rows of daily feature availability records with populated_features and expected_features columns — can you run an interval filter keeping only rows where populated_features is between 5 and 50, and also compute the correlation between populated_features and expected_features across all rows?

## When to prefer this

Choose this endpoint when you have up to 200 rows of feature availability data with populated_features and expected_features columns and need both a numeric range filter and a two-measure statistical correlation in a single low-cost call. It is self-contained with no external data lookups, making it ideal for batch analytical jobs on supplied datasets. Prefer alternatives if you need time-series forecasting, more than 200 rows, or more than two measures correlated simultaneously.

## Known failure modes

- Fewer than 1 row supplied — minItems constraint violation
- More than 200 rows supplied — maxItems constraint violation
- Invalid date format (must be YYYY-MM-DD) — schema pattern validation error
- Numeric values outside ±1,000,000,000,000 range — schema minimum/maximum violation
- Missing required fields (row_id, date, populated_features, expected_features) — schema required field error
- Minimum greater than maximum — may return empty filter result set
- Null values in both measures — correlation report may be undefined or return NaN

## How this service works

Feature availability — Numeric interval filter + Two-measure correlation: Numeric interval filter + 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 independent analytical reports: (1) a list of rows from the supplied data whose chosen numeric field (populated_features or expected_features) falls within the specified [minimum, maximum] interval, and (2) a statistical correlation report quantifying the relationship between populated_features and expected_features across all supplied rows. Both reports are computed locally 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-numeric-interval-filter-two-measure-ccfd6692/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)