# Feature Availability Analytics — Category Frequencies & Numeric Interval Filter

> Feature Availability Analytics — Category Frequencies & Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Computes category frequencies and applies a numeric interval filter on supplied feature availability records (populated_features / expected_features) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-101?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-category-frequencies-numeric-interval-8f469159
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_MAUTiLsagWuu7g8OWpDFo

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-category-frequencies-numeric-interval-8f469159 -d '<json body>'
```

Example prompt: Analyze my feature availability dataset — I have up to 50 rows with populated_features and expected_features per dataset_group and date — give me the category frequencies by group and also filter rows where populated_features falls between 5 and 100.

## When to prefer this

Choose this endpoint when you need two simultaneous lightweight analytical reports — category frequency counts and a numeric range filter — on feature availability data (populated_features / expected_features) without requiring any external data sources, predictions, or live lookups. It is ideal for data quality workflows where you want to audit feature completeness across dataset groups and quickly identify rows within a specific numeric band, all within a single low-cost ($0.01 USDC) call covering up to 200 rows.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Minimum or maximum values outside allowed numeric range — validation error
- Empty rows array — minItems constraint violated
- Payment not included or insufficient — x402 payment required error

## How this service works

Feature availability — Category frequencies + Numeric interval filter: Category frequencies + Numeric interval filter 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) category frequency counts broken down by dataset_group across the supplied rows, and (2) a filtered subset of rows where the selected numeric field (populated_features or expected_features) falls within the specified minimum–maximum interval. Both reports are computed locally on 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"
  },
  "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-category-frequencies-numeric-interval-8f469159/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)