# Join Coverage Daily Sum Series with IQR Outlier Flags

> Join Coverage Daily Sum Series with 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 2026-10-16).

Computes daily sum time series and IQR-based outlier flags for join coverage metrics (matched_rows, source_rows) from supplied row data

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-140?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-daily-sum-series-with-iqr-outlier-flags-fa7cde13
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_86TtL8elaUc0oPiwxGGVu

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-daily-sum-series-with-iqr-outlier-flags-fa7cde13 -d '<json body>'
```

Example prompt: Can you analyze my join coverage data for the 'orders' dataset group — I have daily records from October 2026 with matched_rows and source_rows values — and give me a daily sum series plus flag any outlier days using IQR on the matched_rows field?

## When to prefer this

Choose this endpoint when you have up to 200 rows of join coverage data (matched_rows and source_rows by date and dataset group) and need both a daily sum time series and IQR-based outlier flagging in a single low-cost call. It is ideal for data engineers and analysts who want lightweight, local statistical analysis of pipeline join quality without sending data to an external ML or prediction service. Prefer this over general analytics platforms when cost, simplicity, and offline computation are priorities.

## Known failure modes

- Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Input arrays exceeding 200 rows are rejected
- Null numeric values may affect IQR computation accuracy
- Insufficient data points (e.g. fewer than 4 rows) may produce unreliable IQR statistics
- Payment of 0.01 USDC required per call; missing payment results in 402 response

## How this service works

Join coverage — Daily sum series + IQR outlier flags: Daily sum series + 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 (one for each of the two included report slots): a daily sum time series aggregated from the supplied matched_rows or source_rows values, and an IQR-based outlier flag report identifying which rows or dates fall outside the interquartile range — all computed locally 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-daily-sum-series-with-iqr-outlier-flags-fa7cde13/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)