# Join Coverage Analytics: Category Frequencies + Adjacent Row Changes

> Join Coverage Analytics: Category Frequencies + 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 2026-10-19).

Analyzes join coverage records (matched_rows, source_rows) to produce two local reports: category frequency distribution by dataset_group and adjacent row-to-row change detection for a selected numeric field.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-104?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-category-frequencies-adjacent-row-changes-9b09761e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YicYXxvTGtAOMZPch3hll

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-category-frequencies-adjacent-row-changes-9b09761e -d '<json body>'
```

Example prompt: I have up to 200 join coverage records with matched_rows and source_rows per dataset_group and date — can you run a category frequency breakdown by group and also flag adjacent row changes in the matched_rows field for me?

## When to prefer this

Choose this endpoint when you have join coverage tabular data (with matched_rows and source_rows fields) and need two specific analytical views — category frequency by dataset_group and adjacent row change detection — computed locally without any external API calls, predictions, or live data lookups. It is cost-effective at $0.01 for both reports simultaneously on up to 200 rows. Prefer this over general analytics tools when your data is already structured per the schema and you want fast, deterministic local computation.

## Known failure modes

- Fewer than 1 row submitted returns a validation error
- More than 200 rows submitted exceeds the limit and is rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes schema validation failure
- Invalid date format (not YYYY-MM-DD) triggers a pattern mismatch error
- Numeric values outside ±1,000,000,000,000 are rejected
- Invalid field enum value (not matched_rows or source_rows) causes an error
- Payment of 0.01 USDC not included or rejected results in a 402 response

## How this service works

Join coverage — Category frequencies + Adjacent row changes: Category frequencies + 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 two analytical reports derived entirely from the supplied rows: (1) a category frequency table showing how often each dataset_group value appears, and (2) an adjacent row change report showing delta values between consecutive rows for the selected numeric field (matched_rows or source_rows). No external lookups or predictions are performed; all analysis is local.

## 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-analytics-category-frequencies-adjacent-row-changes-9b09761e/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)