# Join Coverage Analytics — Exact Row Deduplication + Two-Measure Correlation

> Join Coverage Analytics — Exact Row Deduplication + 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).

Deduplicates exact rows and computes correlation between matched_rows and source_rows measures for up to 200 join coverage records, delivering two analytical reports in a single call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-050?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-exact-row-deduplication-two-measure-correlation-9602af43
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6PNFgP6jBKhznl_OP0cKE

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-exact-row-deduplication-two-measure-correlation-9602af43 -d '<json body>'
```

Example prompt: I have join coverage records with matched_rows and source_rows values across groups — can you deduplicate the exact rows and run a correlation analysis between those two measures for my 4 rows: 2026-10-01 row-1 group-a matched=10 source=1, 2026-10-02 row-2 group-b matched=20 source=2, 2026-10-03 row-3 group-a matched=15 source=3, 2026-10-04 row-4 group-b matched=30 source=4?

## When to prefer this

Choose this endpoint when you need both exact row deduplication and a two-measure correlation on join coverage data (matched_rows vs source_rows) in a single low-cost call. It is ideal for data engineering and ETL quality checks where you need to clean duplicates and quantify the relationship between match counts and source counts simultaneously, without sending data to an external ML or analytics platform. Prefer this over general-purpose analytics services when your data is already structured with row_id, dataset_group, date, matched_rows, and source_rows fields and you want deterministic local computation at minimal cost.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema validation failure
- Date not matching YYYY-MM-DD format — pattern validation error
- Numeric values outside allowed range (±1,000,000,000,000) — validation rejection
- All rows are identical (degenerate deduplication case) — may return trivial deduplication result
- Insufficient variance in matched_rows or source_rows for meaningful correlation — correlation may return NaN or undefined

## How this service works

Join coverage — Exact row deduplication + Two-measure correlation: Exact row deduplication + 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 analytical reports: (1) an exact row deduplication report identifying and removing duplicate rows from the submitted join coverage records, and (2) a two-measure Pearson or equivalent correlation report quantifying the statistical relationship between matched_rows and source_rows across the supplied dataset. Both reports are computed 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-exact-row-deduplication-two-measure-correlation-9602af43/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)