# Join Coverage Duplicate Key Report & Adjacent Row Changes

> Join Coverage Duplicate Key Report & Adjacent Row Changes 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).

Analyzes join coverage data rows to produce two reports: a duplicate key report and an adjacent row changes report, for up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-062?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-duplicate-key-report-adjacent-row-changes-5ca90d5d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_OWYanrEUGlgKuPCVFbcF6

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-duplicate-key-report-adjacent-row-changes-5ca90d5d -d '<json body>'
```

Example prompt: Can you run a duplicate key report and adjacent row changes analysis on my join coverage data? I have 4 rows — row-1 through row-4 dated 2026-10-01 to 2026-10-04 with matched_rows values of 10, 20, 15, 30 and source_rows of 1, 2, 3, 4, split across group-a and group-b — analyze the matched_rows field.

## When to prefer this

Choose this endpoint when you need both a duplicate key audit and an adjacent row change analysis in a single call for join coverage data. It is ideal for data engineers validating ETL join outputs, QA workflows checking data integrity before loading, or regression testing join pipelines. It is best when you have up to 200 rows and want two complementary diagnostic reports without making separate API calls.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside the allowed range (-1000000000000 to 1000000000000) are rejected
- Supplying fewer than 1 row returns a validation error
- Invalid field enum value (anything other than matched_rows or source_rows) fails validation

## How this service works

Join coverage — Duplicate key report + Adjacent row changes: Duplicate key report + Adjacent row changes 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

Two independent analytical reports returned together: (1) a duplicate key report identifying any row_ids that appear more than once across the supplied rows, and (2) an adjacent row changes report showing value differences between consecutive rows for the selected field (matched_rows or source_rows). Both reports are computed locally from the supplied data, covering up to 200 rows.

## 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-duplicate-key-report-adjacent-row-changes-5ca90d5d/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)