# Join Coverage — Record Quality Audit + String Normalisation

> Join Coverage — Record Quality Audit + String Normalisation 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 a record quality audit and string normalisation analysis on up to 200 join coverage rows (matched_rows, source_rows), returning 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-001?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-string-normalisation-a2b72a16
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YnzVedQRlDO9PLaNI5rHN

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-string-normalisation-a2b72a16 -d '<json body>'
```

Example prompt: Can you run a record quality audit and string normalisation check on my join coverage data? I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and matched_rows/source_rows values like 10/1, 20/2, 15/3, 30/4.

## When to prefer this

Choose this endpoint when you need both a record quality audit and string normalisation analysis on join coverage data (matched_rows vs source_rows) in a single low-cost call. It is ideal for data engineers and analysts running post-join QA checks on small-to-medium batches (up to 200 rows) without needing live lookups, predictions, or external data enrichment. Prefer it over broader analytics endpoints when your primary concern is join coverage integrity and string consistency.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes validation error
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) rejected
- Empty rows array (minItems: 1 not met) causes error
- Non-numeric or non-null matched_rows/source_rows values rejected

## How this service works

Join coverage — Record quality audit + String normalisation: Record quality audit + String normalisation 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

Two independent analytical reports: (1) a record quality audit covering anomalies, completeness, and consistency across the supplied join coverage rows; (2) a string normalisation report identifying and correcting string formatting issues in fields like dataset_group and row_id. Both reports are computed locally against 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",
     "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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-record-quality-audit-string-normalisation-a2b72a16/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)