# Join Coverage Record Quality Audit, String Normalisation & Base-100 Series

> Join Coverage Record Quality Audit, String Normalisation & Base-100 Series 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 three analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation check, and base-100 indexed series, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-201?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-base-100-series-90dd4b57
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XPUwGBmsgbz53Bg2uaHOB

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-base-100-series-90dd4b57 -d '<json body>'
```

Example prompt: Can you run all three join coverage reports — record quality audit, string normalisation, and base-100 series — on this dataset of 4 rows: row-1 (group-a, 2026-10-01, source_rows=1, matched_rows=10), row-2 (group-b, 2026-10-02, source_rows=2, matched_rows=20), row-3 (group-a, 2026-10-03, source_rows=3, matched_rows=15), row-4 (group-b, 2026-10-04, source_rows=4, matched_rows=30), analysing the matched_rows field with base index at position 0?

## When to prefer this

Choose this endpoint when you need a combined, cost-efficient bundle of three data-quality analyses on join coverage records in a single call: quality auditing, string normalisation, and base-100 series indexing. It is ideal for ETL validation workflows, data pipeline regression checks, and coverage monitoring where you want all three reports without separate API calls. Prefer it over individual analytics endpoints when your dataset has up to 200 rows and you want offline, deterministic analysis with no external data dependencies.

## Known failure modes

- Fewer than 1 row or more than 200 rows supplied — validation error
- base_index out of range (0–199) or exceeding array length — error response
- Malformed date strings not matching YYYY-MM-DD pattern — schema validation failure
- matched_rows or source_rows values outside allowed numeric range — rejection
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema error
- Payment of 0.01 USDC not received via x402 — 402 Payment Required response

## How this service works

Join coverage — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 on the supplied join coverage rows: (1) a record quality audit flagging completeness, consistency, and anomalies in matched_rows and source_rows; (2) a string normalisation report assessing and cleaning dataset_group and row_id fields; and (3) a base-100 indexed series for the chosen numeric field (matched_rows or source_rows), rebased from the specified base_index row. All analysis is performed locally on the submitted 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-record-quality-audit-string-normalisation-base-100-series-90dd4b57/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)