# Schema Conformity Analytics — String Normalisation + Category Frequencies

> Schema Conformity Analytics — String Normalisation + Category Frequencies 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).

Analyzes schema conformity records (valid_records, checked_records) to produce two analytical reports: string normalization and category frequency breakdowns, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-024?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/schema-conformity-analytics-string-normalisation-category-frequencies-c7b0d7a6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_9dU2fK4Vuwp0yGdhlSbLQ

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 schema-conformity-analytics-string-normalisation-category-frequencies-c7b0d7a6 -d '<json body>'
```

Example prompt: I have 4 schema conformity records — row-1 on 2026-10-01 in group-a with 10 valid out of 1 checked, row-2 on 2026-10-02 in group-b with 20 valid out of 2 checked, row-3 on 2026-10-03 in group-a with 15 valid out of 3 checked, and row-4 on 2026-10-04 in group-b with 30 valid out of 4 checked — can you run string normalization and category frequency analysis on these?

## When to prefer this

Choose this endpoint when you need two fast, offline analytical reports — string normalization and category frequency distribution — on schema conformity data containing valid_records and checked_records fields, for batches of up to 200 rows, at a low flat cost of $0.01 USDC. It is ideal when you do not need external lookups, predictions, or ML inference, just deterministic local computation on your own conformity records.

## Known failure modes

- Empty or missing rows array returns an error
- More than 200 rows submitted exceeds the maxItems limit and will be rejected
- Rows missing required fields (row_id, dataset_group, date, valid_records, checked_records) fail schema validation
- Date strings not matching YYYY-MM-DD pattern are rejected
- Payment of 0.01 USDC not included or declined results in 402 response
- Numeric values outside the ±1,000,000,000,000 range are rejected

## How this service works

Schema conformity — String normalisation + Category frequencies: String normalisation + Category frequencies for supplied schema conformity records (valid_records, checked_records). 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 analytical reports: (1) a string normalization report that standardizes and cleans text values across the submitted rows, and (2) a category frequency report showing count distributions across dataset_group values and other categorical fields — both computed locally without any external lookups, covering up to 200 submitted rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-analytics-string-normalisation-category-frequencies-c7b0d7a6/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)