# Null Coverage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags

> Null Coverage Analytics — Record Quality Audit, 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 2026-10-18).

Audits null/missing-value coverage records by performing record quality checks, string normalisation, and IQR-based outlier flagging across up to 200 rows in three independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-03-203?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/null-coverage-analytics-record-quality-audit-string-normalisation-iqr-f8f76d17
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_MKl4jDcASH7qKTpVQ5fJh

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 null-coverage-analytics-record-quality-audit-string-normalisation-iqr-f8f76d17 -d '<json body>'
```

Example prompt: Can you run the null coverage quality audit on my dataset records — I've got rows with missing_values and total_values for groups 'group-a' and 'group-b' across dates in October 2026, and I want the record quality audit, string normalisation, and IQR outlier flags all in one go for the missing_values field?

## When to prefer this

Choose this endpoint when you need simultaneous record quality auditing, string normalisation, and IQR outlier detection on null/missing-value coverage data in a single low-cost call. It is purpose-built for datasets with missing_values and total_values columns grouped by dataset_group and date. Prefer it over generic data quality tools when your data is already structured in this schema and you want three bundled analytical perspectives for $0.01 USDC without any external lookups or predictions.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit, request rejected
- missing required fields (row_id, date, missing_values, total_values) — row validation fails
- date not in YYYY-MM-DD format — pattern validation error
- numeric values outside ±1,000,000,000,000 range — schema rejection
- empty rows array — minItems:1 violated
- invalid field enum value other than 'missing_values' or 'total_values' — schema error

## How this service works

Null coverage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied null coverage records (missing_values, total_values). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Returns three independent analytical reports for the submitted rows: (1) a record quality audit highlighting completeness and integrity issues, (2) a string normalisation report standardising text fields like dataset_group, and (3) IQR-based outlier flags identifying anomalous values in the selected numeric field (missing_values or total_values). Covers up to 200 input rows per call.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "missing_values",
     "total_values"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_values": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "missing_values": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_values": 1,
     "dataset_group": "group-a",
     "missing_values": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_values": 2,
     "dataset_group": "group-b",
     "missing_values": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_values": 3,
     "dataset_group": "group-a",
     "missing_values": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_values": 4,
     "dataset_group": "group-b",
     "missing_values": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "missing_values",
    "total_values"
   ],
   "type": "string",
   "default": "missing_values"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/null-coverage-analytics-record-quality-audit-string-normalisation-iqr-f8f76d17/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)