# Join Coverage Record Quality Audit and Missing-Value Fill

> Join Coverage Record Quality Audit and Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Audits join coverage records (matched_rows, source_rows) for quality issues and fills missing values using a chosen imputation method, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-019?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-record-quality-audit-and-missing-value-fill-0524d1bb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hCb1STjavkIWEfdbW1HbU

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-and-missing-value-fill-0524d1bb -d '<json body>'
```

Example prompt: Can you audit these join coverage records for data quality issues and fill any missing matched_rows values using median imputation? Here are up to 50 rows with row_id, dataset_group, date, matched_rows, and source_rows — give me both the quality audit and the filled data.

## When to prefer this

Choose this endpoint when you need both a data quality audit and missing-value imputation for join coverage metrics (matched_rows, source_rows) in a single call. It is ideal for ETL validation, pre-reporting data cleaning, and data warehouse maintenance tasks where you have up to 200 rows and want two complementary analytical reports without any external lookups or live data dependencies. Prefer it over general-purpose analytics endpoints when your specific domain is join coverage statistics and you need imputation method control (zero, mean, or median).

## Known failure modes

- Rows array empty or missing — validation error returned
- More than 200 rows submitted — request rejected with limit error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Invalid method value outside zero/mean/median enum — rejected
- Invalid field value outside matched_rows/source_rows enum — rejected
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Payment not provided or insufficient — x402 payment required response

## How this service works

Join coverage — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill 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 two independent reports: (1) a record quality audit highlighting issues such as nulls, anomalies, or constraint violations in the join coverage rows, and (2) an explicit missing-value fill report showing which matched_rows or source_rows fields were null and what values were imputed using the chosen method (zero, mean, or median). 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",
     "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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-record-quality-audit-and-missing-value-fill-0524d1bb/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)