# Feature Availability Analytics: Numeric Interval Filter + Adjacent Row Changes

> Feature Availability Analytics: Numeric Interval Filter + 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 by filtering rows within a numeric interval and detecting adjacent row changes across populated_features or expected_features fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-127?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-adjacent-row-a890e9b6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7jNEgFWdzy33FOp-NVbiC

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-adjacent-row-a890e9b6 -d '<json body>'
```

Example prompt: Run a numeric interval filter and adjacent row change analysis on my feature availability records — filter the populated_features field to values between 5 and 100, and send these rows: row-1 on 2026-10-01 in group-a with populated_features=10 and expected_features=1, row-2 on 2026-10-02 in group-b with populated_features=20 and expected_features=2, row-3 on 2026-10-03 in group-a with populated_features=15 and expected_features=3.

## When to prefer this

Choose this endpoint when you need two simultaneous local analytical reports — interval filtering and adjacent row change detection — on feature availability data, without needing live lookups or external data enrichment. Ideal for data quality pipelines, feature population audits, and batch dataset inspection tasks up to 200 rows at $0.01 USDC per call.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required row fields (row_id, date, populated_features, expected_features) causes request rejection
- Date values not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 range fail schema validation
- Empty rows array returns an error
- Invalid field enum value (not 'populated_features' or 'expected_features') causes failure

## How this service works

Feature availability — Numeric interval filter + Adjacent row changes: Numeric interval filter + 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) rows from the input whose specified field value falls within the supplied numeric interval [minimum, maximum], and (2) a detection of adjacent row-to-row changes in the specified field across the supplied dataset — up to 200 rows processed per call.

## 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-adjacent-row-a890e9b6/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)