# Join Coverage Record Quality Audit with IQR Outlier Flags

> Join Coverage Record Quality Audit with IQR Outlier Flags 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).

Performs a record quality audit and IQR-based outlier detection on supplied join coverage data (matched_rows, source_rows) for up to 200 rows, returning 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-014?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/join-coverage-record-quality-audit-with-iqr-outlier-flags-63572191
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_h2rwkQXsbPHuavTbA5TlV

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 join-coverage-record-quality-audit-with-iqr-outlier-flags-63572191 -d '<json body>'
```

Example prompt: Can you run a record quality audit and IQR outlier detection on my join coverage data — I have 4 rows across two groups (group-a and group-b) with matched_rows and source_rows counts for dates in October 2026, and I want to flag outliers in the matched_rows field?

## When to prefer this

Choose this endpoint when you need a combined record quality audit AND IQR outlier detection on join coverage metrics in a single API call. It is ideal for data engineers and analysts who want to validate ETL join outputs, detect anomalous match rates, or audit data pipeline quality without external dependencies. Prefer it over generic stats APIs when your data specifically involves matched_rows and source_rows join coverage fields and you need two structured reports for up to 200 rows at low cost.

## Known failure modes

- Fewer than 1 row supplied — minimum input not met
- More than 200 rows submitted — exceeds max item limit
- Missing required fields (row_id, date, matched_rows, source_rows, dataset_group) in any row
- Date field not in YYYY-MM-DD format — pattern validation failure
- Numeric values out of allowed range (±1 trillion)
- Invalid field enum value — must be 'matched_rows' or 'source_rows'
- Payment not completed — x402 protocol payment required before response

## How this service works

Join coverage — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied join coverage records (matched_rows, source_rows). 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 2 independent analytical reports: (1) a record quality audit summarizing completeness, validity, and structural issues across the supplied rows, and (2) IQR-based outlier flags identifying which rows have anomalous values in the selected field (matched_rows or source_rows), all computed locally without external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "matched_rows",
     "source_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "matched_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_rows": 1,
     "matched_rows": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_rows": 2,
     "matched_rows": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_rows": 3,
     "matched_rows": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_rows": 4,
     "matched_rows": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "matched_rows",
    "source_rows"
   ],
   "type": "string",
   "default": "matched_rows"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-record-quality-audit-with-iqr-outlier-flags-63572191/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)