# Feature Availability Analytics — Quality Audit, Deduplication & Grouped Summary

> Feature Availability Analytics — Quality Audit, Deduplication & Grouped Summary 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).

Runs three local analytical reports on feature availability records: a record quality audit, exact row deduplication, and a grouped numeric summary of populated_features or expected_features values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-211?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-quality-audit-deduplication-grouped-c610579d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_mzJxZ1N3nTexnDeoQilLp

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-quality-audit-deduplication-grouped-c610579d -d '<json body>'
```

Example prompt: Can you run a quality audit, exact deduplication check, and grouped numeric summary on these feature availability records — up to 200 rows with row_id, dataset_group, date, populated_features, and expected_features — and focus the summary on the populated_features field?

## When to prefer this

Choose this endpoint when you need all three of quality audit, exact deduplication, and grouped numeric summarization on feature availability data in a single $0.01 call (up to 200 rows), without requiring any external data lookups, live APIs, or prediction services. It is ideal for pre-processing feature store records, validating ML training data, or auditing feature tracking logs entirely offline.

## Known failure modes

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

## How this service works

Feature availability — Record quality audit + Exact row deduplication + Grouped numeric summary: Record quality audit + Exact row deduplication + Grouped numeric summary for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports: (1) a record quality audit flagging nulls, format errors, and data issues; (2) an exact row deduplication report identifying and removing duplicate records; and (3) a grouped numeric summary (e.g. sum, mean, count) of the selected field (populated_features or expected_features) broken down by dataset_group and/or date. All computed locally with no external calls.

## 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-quality-audit-deduplication-grouped-c610579d/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)