# Join Coverage Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication

> Join Coverage Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication 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 record quality, normalises strings, and deduplicates rows for up to 200 join coverage records (matched_rows, source_rows) in three independent local analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-191?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-analytics-record-quality-audit-string-normalisation-ed24299a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_AEc88kbNfh0hZ6ka830Ie

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-ed24299a -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and exact row deduplication on my join coverage data? I have rows with row_id, dataset_group, date (YYYY-MM-DD), matched_rows, and source_rows — here are up to 200 records I'd like all three reports for.

## When to prefer this

Choose this endpoint when you need three combined data-quality operations — record quality audit, string normalisation, and exact row deduplication — applied together to join coverage records in a single cost-effective call. It is ideal for data engineers validating ETL join outputs, analysts cleaning join coverage exports before reporting, or pipelines that need lightweight local analytics without any external API lookups or predictions. Prefer this over single-report endpoints when all three operations are needed simultaneously and your dataset is 200 rows or fewer.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- row_id or dataset_group exceeding 80 character limit — schema validation failure
- matched_rows or source_rows outside allowed numeric range — schema validation failure
- Empty rows array — minItems:1 violation

## How this service works

Join coverage — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 data issues in the join coverage records, (2) a string normalisation report cleaning and standardising text fields like dataset_group, and (3) an exact row deduplication report identifying and removing fully duplicate rows — all computed 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-record-quality-audit-string-normalisation-ed24299a/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)