# Join Coverage String Normalisation and Secondary-Measure Weighted Mean

> Join Coverage String Normalisation and Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).

Computes string normalisation and secondary-measure weighted mean analytics for join coverage records (matched_rows, source_rows) across up to 200 rows, returning 2 independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-034?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-string-normalisation-and-secondary-measure-weighted-mean-1910cfa7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_IIPISfYs5-KbcvTi5HAwo

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-secondary-measure-weighted-mean-1910cfa7 -d '<json body>'
```

Example prompt: Can you run string normalization and a secondary-measure weighted mean on my join coverage data? I have 4 rows — group-a on 2026-10-01 with matched_rows=10 and source_rows=1, group-b on 2026-10-02 with matched_rows=20 and source_rows=2, group-a on 2026-10-03 with matched_rows=15 and source_rows=3, and group-b on 2026-10-04 with matched_rows=30 and source_rows=4 — and I want to analyze the matched_rows field.

## When to prefer this

Choose this endpoint when you need both string normalisation and a secondary-measure weighted mean computed together on join coverage data (matched_rows, source_rows) in a single low-cost call. It is ideal for ETL pipeline auditing, data quality checks on join operations, or summarising coverage ratios across dataset groups. Prefer this over histogram or ordering endpoints when you specifically need weighted averaging and string normalisation as your two analytical outputs.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Empty rows array (minItems=1 not met) — request rejected
- Invalid field enum value (not 'matched_rows' or 'source_rows') — rejected

## How this service works

Join coverage — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean 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: (1) string normalisation results for the join coverage records, and (2) a secondary-measure weighted mean computed over the selected field (matched_rows or source_rows), both derived locally from the supplied rows without 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-secondary-measure-weighted-mean-1910cfa7/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)