# Join Coverage Analytics: Numeric Interval Filter + Two-Measure Correlation

> Join Coverage Analytics: Numeric Interval Filter + 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).

Filters join coverage rows by a numeric interval on a chosen measure and computes correlation between matched_rows and source_rows for supplied dataset records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-131?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-numeric-interval-filter-two-measure-correlation-51f11c66
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_RxBiHNlS2sQhEfdDFuxJW

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-numeric-interval-filter-two-measure-correlation-51f11c66 -d '<json body>'
```

Example prompt: Run a join coverage analysis on my dataset: I have rows with matched_rows and source_rows values, and I want to filter where matched_rows is between 5 and 150, then compute the correlation between matched_rows and source_rows across all my records — here are the rows: [{row_id: 'r1', date: '2025-01-10', dataset_group: 'group-a', matched_rows: 80, source_rows: 100}, {row_id: 'r2', date: '2025-01-11', dataset_group: 'group-b', matched_rows: 10, source_rows: 200}].

## When to prefer this

Use this endpoint when you have pre-collected join coverage data (matched_rows and source_rows per record) and need both a numeric range filter and a correlation analysis in a single $0.01 call. Ideal for data engineers and analytics pipelines that want to QA join quality, detect anomalies, or measure the statistical relationship between source and matched row counts without sending data to an external live system. Prefer this over general-purpose analytics tools when you need offline, deterministic computation on up to 200 rows.

## Known failure modes

- Input array is empty or missing — returns validation error
- Rows exceed 200 item limit — request rejected
- minimum greater than maximum — may return empty filtered set or validation error
- Missing required fields (row_id, date, matched_rows, source_rows, dataset_group) — schema validation failure
- Null values in both measure fields — correlation computation may be undefined or return NaN
- Date field not matching YYYY-MM-DD pattern — schema validation error

## How this service works

Join coverage — Numeric interval filter + Two-measure correlation: Numeric interval filter + 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) rows from the input dataset that fall within the specified numeric interval for the chosen field (matched_rows or source_rows), and (2) a correlation analysis between matched_rows and source_rows across the supplied records. Results are based solely on the supplied data — no external lookups or live data.

## 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-numeric-interval-filter-two-measure-correlation-51f11c66/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)