# Feature Availability Analytics: Numeric Distribution & Adjacent Row Changes

> Feature Availability Analytics: Numeric Distribution & 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 2026-10-20).

Analyzes feature availability records (populated_features, expected_features) to produce two reports: a numeric distribution histogram and an adjacent row change analysis, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-077?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-distribution-adjacent-row-dcd759b5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hRAyz05wxXSi7ZTqV6g6D

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-distribution-adjacent-row-dcd759b5 -d '<json body>'
```

Example prompt: Analyze the numeric distribution and adjacent row changes for my feature availability data — here are 4 rows across two groups: row-1 on 2026-10-01 for group-a with populated_features=10 and expected_features=1, row-2 on 2026-10-02 for group-b with populated_features=20 and expected_features=2, row-3 on 2026-10-03 for group-a with populated_features=15 and expected_features=3, row-4 on 2026-10-04 for group-b with populated_features=30 and expected_features=4; run both reports on the populated_features field.

## When to prefer this

Choose this endpoint when you need fast, offline statistical analysis of feature availability records — specifically a numeric distribution plus adjacent row change report — without any live data lookup or prediction. Ideal for data quality auditing, MLOps pipelines, and dataset observability workflows where you supply the rows directly (up to 200) and want two bundled analytical reports at minimal cost ($0.01 USDC).

## Known failure modes

- Fewer than 1 row supplied — validation error
- More than 200 rows supplied — exceeds maxItems limit, request rejected
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, date, populated_features, expected_features) — request rejected
- Invalid field enum value (not 'populated_features' or 'expected_features') — schema error
- Null values in numeric fields may affect distribution computation accuracy

## How this service works

Feature availability — Numeric distribution + Adjacent row changes: Numeric distribution + 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

Returns two independent analytical reports: (1) a numeric distribution/histogram of the selected field (populated_features or expected_features) across the supplied rows, showing spread, frequency bins, and summary statistics; (2) an adjacent row changes report showing deltas between consecutive rows, highlighting shifts in feature availability over time. Both reports are computed locally from the supplied data 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-numeric-distribution-adjacent-row-dcd759b5/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)