# Join Coverage Analytics — Exact Row Deduplication + Missing-Value Fill

> Join Coverage Analytics — Exact Row Deduplication + Missing-Value Fill 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).

Deduplicates exact rows and fills missing values in join coverage data (matched_rows, source_rows), producing 2 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-054?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-exact-row-deduplication-missing-value-fill-c853eba9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_jKmyywls5-IkkXYl0ROYl

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-exact-row-deduplication-missing-value-fill-c853eba9 -d '<json body>'
```

Example prompt: Clean up my join coverage data for group-a and group-b across October 2026 — deduplicate exact rows and fill any missing matched_rows values using median imputation. Here are 4 rows with row IDs row-1 through row-4.

## When to prefer this

Choose this endpoint when you have raw join coverage tabular data (matched_rows, source_rows) that needs exact row deduplication and missing-value imputation before analytics or pipeline processing. It is ideal for small-to-medium datasets (up to 200 rows) where you want two independent local analytical reports without any external lookup, prediction, or live data access. Prefer this over general-purpose data tools when you specifically need join coverage metrics cleaned with controlled fill strategies (zero, mean, or median).

## Known failure modes

- Input exceeds 200 rows — request rejected
- Row missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Invalid method enum value outside zero/mean/median — rejected
- Invalid field value outside matched_rows/source_rows — rejected
- Empty rows array — rejected

## How this service works

Join coverage — Exact row deduplication + Explicit missing-value fill: Exact row deduplication + Explicit missing-value fill 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 independent analytical reports covering: (1) exact duplicate row identification and removal results, and (2) explicit missing-value fill results for the chosen field (matched_rows or source_rows) using the selected method (zero, mean, or median), applied across up to 200 supplied join coverage records.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-exact-row-deduplication-missing-value-fill-c853eba9/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)