# Join Coverage Analytics: Record Quality Audit, Exact Row Deduplication, and Numeric Interval Filter

> Join Coverage Analytics: Record Quality Audit, Exact Row Deduplication, and Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Performs three local analytical reports on join coverage data — record quality audit, exact row deduplication, and numeric interval filtering — on up to 200 rows of matched_rows/source_rows data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-214?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-record-quality-audit-exact-row-deduplication-04fb45b5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_CHkGAdvjOOpqnr_Zn9FF3

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-exact-row-deduplication-04fb45b5 -d '<json body>'
```

Example prompt: Can you run a record quality audit, exact row deduplication, and numeric interval filter on my join coverage data? I have rows with matched_rows and source_rows fields — filter on matched_rows between 0 and 100, and here are my 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).

## When to prefer this

Choose this endpoint when you have join coverage data (matched_rows and source_rows per row) and need all three of: a record quality audit, exact deduplication, and numeric range filtering in a single low-cost call. It is ideal for data engineering pipelines that need lightweight, local analytical checks without any external data lookups, predictions, or live APIs. Best for datasets up to 200 rows where cost efficiency ($0.01 for three reports) matters.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Minimum greater than maximum may produce an empty interval filter result
- Empty rows array (minItems: 1 not met) causes an error

## How this service works

Join coverage — Record quality audit + Exact row deduplication + Numeric interval filter: Record quality audit + Exact row deduplication + Numeric interval filter 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 for the supplied join coverage rows: (1) a record quality audit flagging missing, malformed, or suspect values; (2) an exact row deduplication report identifying and removing duplicate records; and (3) a numeric interval filter report returning only rows where the selected field (matched_rows or source_rows) falls within the specified minimum/maximum range. All processing is local 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"
  },
  "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-record-quality-audit-exact-row-deduplication-04fb45b5/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)