# Join Coverage Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary

> Join Coverage Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).

Runs three offline analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and grouped numeric summary — up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-194?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-record-quality-audit-string-normalisation-9631b82e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vsvpx0FS3jsko4dcnZNhP

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-9631b82e -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and grouped numeric summary on my join coverage data? I have 4 rows with dataset groups 'group-a' and 'group-b', matched_rows ranging from 10 to 30, and source_rows from 1 to 4 — use matched_rows as the primary field.

## When to prefer this

Choose this endpoint when you need three combined analytical operations on join coverage data in a single call — quality auditing, string normalisation, and grouped numeric summarisation — without making external API calls or requiring live data. Ideal for data engineering pipelines that need lightweight, offline validation and summarisation of match/source row metrics grouped by dataset category.

## Known failure modes

- Submitting more than 200 rows causes rejection or truncation
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes validation errors
- Invalid date format (non YYYY-MM-DD) causes schema rejection
- Numeric values outside allowed range cause validation failure
- Empty rows array returns an error

## How this service works

Join coverage — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary 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

Returns three independent analytical reports for the supplied join coverage rows: (1) a record quality audit flagging missing values, anomalies, and completeness issues; (2) a string normalisation report cleaning dataset_group and other string fields; (3) a grouped numeric summary (e.g. counts, means, totals) of the selected numeric field (matched_rows or source_rows) broken down by dataset_group. All computed locally 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-9631b82e/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)