# Null Coverage String Normalisation and Keyed Dataset Comparison

> Null Coverage String Normalisation and Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).

Analyzes null/missing value coverage across two supplied datasets (up to 200 rows each) using string normalization and keyed group comparison, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-03-036?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/null-coverage-string-normalisation-and-keyed-dataset-comparison-69b481f1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_AeWux-qXh5BpHx3lEjqHN

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-string-normalisation-and-keyed-dataset-comparison-69b481f1 -d '<json body>'
```

Example prompt: I have two snapshots of my pipeline data — each up to 200 rows with row IDs, dataset groups, dates, missing values, and total values — can you run a null coverage comparison between them and give me two independent reports showing where missing values differ by group?

## When to prefer this

Choose this endpoint when you have two structured datasets with explicit null/missing value counts per row and want a normalized, keyed group comparison without any external data fetching, live lookup, or ML prediction. Ideal for data quality audits, pipeline regression checks, and completeness reporting on supplied data up to 200 rows per dataset.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) in any row — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Empty rows array (minItems: 1 not met) — error returned
- Payment of 0.01 USDC not included or fails — 402 Payment Required

## How this service works

Null coverage — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied null coverage records (missing_values, total_values). 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

Two independent null coverage analytical reports derived from the two supplied row arrays, including string-normalized group key comparisons, missing value counts, total value counts, and coverage statistics per group and date. 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",
     "missing_values",
     "total_values"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_values": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "missing_values": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_values": 1,
     "dataset_group": "group-a",
     "missing_values": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_values": 2,
     "dataset_group": "group-b",
     "missing_values": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_values": 3,
     "dataset_group": "group-a",
     "missing_values": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_values": 4,
     "dataset_group": "group-b",
     "missing_values": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "other": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "missing_values",
     "total_values"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "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
   },
   "de
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/null-coverage-string-normalisation-and-keyed-dataset-comparison-69b481f1/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)