# Join Coverage Analytics: String Normalisation + IQR Outlier Flags

> Join Coverage Analytics: String Normalisation + IQR Outlier Flags 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).

Normalises string fields and flags IQR-based outliers in join coverage records (matched_rows, source_rows) for up to 200 rows, producing 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-032?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-analytics-string-normalisation-iqr-outlier-flags-deb6f9e4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vrX4gxSjkEivvJm0_LKrP

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-string-normalisation-iqr-outlier-flags-deb6f9e4 -d '<json body>'
```

Example prompt: Can you run string normalisation and IQR outlier detection on my join coverage data? I have 4 rows for group-a and group-b from 2026-10-01 to 2026-10-04 with matched_rows values of 10, 20, 15, and 30 — flag any outliers in the matched_rows field.

## When to prefer this

Choose this endpoint when you need lightweight, local statistical analysis of join coverage metrics without any external dependencies or live data lookups. It is ideal for data engineers who want to quickly audit matched_rows and source_rows for anomalies and normalise string fields in a single low-cost call. Prefer it over general-purpose analytics platforms when you want a deterministic, reproducible report on a small-to-medium batch (up to 200 rows) at minimal cost ($0.01 USDC for 2 reports).

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — rejected with validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema validation failure
- Date not in YYYY-MM-DD format — pattern validation error
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Invalid field enum value (not matched_rows or source_rows) — validation error
- Insufficient data variance for meaningful IQR computation — may return degenerate outlier flags

## How this service works

Join coverage — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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 per call: (1) a string normalisation report covering the dataset_group and related string fields, and (2) an IQR outlier flag report marking which rows have statistically anomalous values in the selected numeric field (matched_rows or source_rows). No external lookups or predictions are made; all analysis is performed locally on the supplied data.

## 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-string-normalisation-iqr-outlier-flags-deb6f9e4/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)