# Legal Document Inventories — String Normalisation + Two-Measure Correlation

> Legal Document Inventories — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).

Normalises string fields and computes two-measure correlation analysis on legal document inventory records (document_count, reviewed_count) for up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-writing-10-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/legal-document-inventories-string-normalisation-two-measure-correlation-e607ce07
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2Jg9y804Itsqqwmq7bKxH

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 legal-document-inventories-string-normalisation-two-measure-correlation-e607ce07 -d '<json body>'
```

Example prompt: I have a legal document inventory with 4 rows — row-1 on 2026-10-01 in group-a with 10 documents and 1 reviewed, row-2 on 2026-10-02 in group-b with 20 documents and 2 reviewed, row-3 on 2026-10-03 in group-a with 15 documents and 3 reviewed, and row-4 on 2026-10-04 in group-b with 30 documents and 4 reviewed — can you normalise the string fields and run a two-measure correlation between document_count and reviewed_count?

## When to prefer this

Choose this endpoint when you have legal document inventory data with both a string field (document_group) needing normalisation and two numeric measures (document_count, reviewed_count) you want to correlate statistically, with up to 200 rows, and you need two independent reports in a single low-cost call without any external data lookup or professional legal assessment.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, document_group, date, document_count, reviewed_count) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Null values for both numeric measures on all rows — correlation cannot be computed
- row_id exceeds 80 characters — schema validation error
- Payment not included or insufficient — x402 payment required error

## How this service works

Legal document inventories — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied legal document inventories records (document_count, reviewed_count). 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 per call: (1) normalised string fields for document_group and other text columns, and (2) a two-measure correlation analysis between document_count and reviewed_count, computed locally without any external lookup or prediction, covering up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/legal-document-inventories-string-normalisation-two-measure-correlation-e607ce07/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)