# Join Coverage Analytics: Record Quality Audit, String Normalisation & Numeric Distribution

> Join Coverage Analytics: Record Quality Audit, String Normalisation & Numeric Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Runs three independent analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and numeric distribution analysis — all locally, on up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-193?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-record-quality-audit-string-normalisation-bb94ec76
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gUu3SWn874gaHiNkNrNSd

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-analytics-record-quality-audit-string-normalisation-bb94ec76 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and numeric distribution report on my join coverage data? Here are 4 rows: row-1 on 2026-10-01 in group-a with source_rows=1 and matched_rows=10, row-2 on 2026-10-02 in group-b with source_rows=2 and matched_rows=20, row-3 on 2026-10-03 in group-a with source_rows=3 and matched_rows=15, row-4 on 2026-10-04 in group-b with source_rows=4 and matched_rows=30 — analyse the matched_rows field.

## When to prefer this

Choose this endpoint when you have up to 200 join coverage records and need three simultaneous analytical perspectives — quality audit, string normalisation, and numeric distribution — in a single call without any external lookups or live data enrichment. It is ideal for offline ETL validation workflows, batch data quality reviews, and situations where all data is already in hand and needs structured local analysis for a flat $0.01 USDC fee.

## Known failure modes

- More than 200 rows submitted — exceeds the per-call row limit
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row
- Invalid date format (must match YYYY-MM-DD pattern)
- Numeric values out of the allowed range (±1,000,000,000,000)
- Empty rows array — minItems:1 not satisfied
- Invalid field enum value (must be 'matched_rows' or 'source_rows')

## How this service works

Join coverage — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution for supplied join coverage records (matched_rows, source_rows). 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 covering: (1) a record quality audit flagging completeness and validity issues across the supplied rows, (2) string normalisation analysis of text fields like dataset_group, and (3) a numeric distribution summary of the selected field (matched_rows or source_rows). All analysis is performed 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",
     "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-analytics-record-quality-audit-string-normalisation-bb94ec76/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)