# Join Coverage Analytics: String Normalisation + Two-Measure Correlation

> Join Coverage Analytics: String Normalisation + Two-Measure Correlation 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).

Computes string normalisation and two-measure correlation analysis on supplied join coverage records (matched_rows, source_rows) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-033?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-string-normalisation-two-measure-correlation-02f48c79
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_q9iY1CYl_bh0JbZ-4uTEa

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-string-normalisation-two-measure-correlation-02f48c79 -d '<json body>'
```

Example prompt: Can you run string normalisation and two-measure correlation on my join coverage data? I have 4 rows: group-a on 2026-10-01 had 10 matched out of 1 source, group-b on 2026-10-02 had 20 matched out of 2 source, group-a on 2026-10-03 had 15 matched out of 3 source, and group-b on 2026-10-04 had 30 matched out of 4 source.

## When to prefer this

Choose this endpoint when you need to simultaneously normalise join coverage string data and compute a two-measure correlation between matched_rows and source_rows in a single call, without requiring any external data sources. Best suited for data engineers auditing ETL pipeline join quality or data quality analysts checking dataset merge efficiency across groups, especially when both analytical reports are needed together at low cost ($0.01 USDC for both).

## Known failure modes

- More than 200 rows submitted — request will be rejected due to maxItems constraint
- Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null or out-of-range numeric values for matched_rows or source_rows may affect correlation computation
- Empty rows array (minItems: 1) will be rejected

## How this service works

Join coverage — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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

Returns two independent analytical reports: (1) a string normalisation report on the join coverage records and (2) a two-measure correlation analysis between matched_rows and source_rows, computed entirely from 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-string-normalisation-two-measure-correlation-02f48c79/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)