# Join Coverage String Normalisation and Adjacent Row Changes Analyzer

> Join Coverage String Normalisation and Adjacent Row Changes Analyzer is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Analyzes join coverage records by performing string normalization and detecting adjacent row changes across matched_rows and source_rows fields, producing 2 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-029?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-string-normalisation-and-adjacent-row-changes-analyzer-ae685bb0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_dV7vyFLVKbYnyfec_ZVFY

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-string-normalisation-and-adjacent-row-changes-analyzer-ae685bb0 -d '<json body>'
```

Example prompt: Analyze my join coverage records for string normalization issues and adjacent row changes — I have 4 rows for groups A and B dated 2026-10-01 through 2026-10-04 with matched_rows values of 10, 20, 15, 30 and source_rows of 1, 2, 3, 4. Run the analysis on the matched_rows field.

## When to prefer this

Choose this endpoint when you need lightweight, local analytical processing of join coverage tabular data — specifically string normalization of group labels and adjacent row change detection — without requiring external data sources, live lookups, or predictions. Ideal for ETL quality checks, data pipeline audits, and pre-report data validation on small to medium datasets (up to 200 rows).

## Known failure modes

- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Empty rows array (minItems:1 not satisfied) — request rejected

## How this service works

Join coverage — String normalisation + Adjacent row changes: String normalisation + 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

Returns 2 analytical reports per call: (1) a string normalization report covering the dataset_group and row label fields, and (2) an adjacent row changes report highlighting step-changes between consecutive rows in the selected field (matched_rows or source_rows). Reports are generated 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
  },
  "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-string-normalisation-and-adjacent-row-changes-analyzer-ae685bb0/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)