# Metadata Completeness Analyzer — Adjacent Row Changes & Secondary-Measure Weighted Mean

> Metadata Completeness Analyzer — Adjacent Row Changes & Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).

Computes adjacent row changes and secondary-measure weighted mean for metadata completeness records (present_fields, expected_fields) across up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-web-05-159?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/metadata-completeness-analyzer-adjacent-row-changes-secondary-measure-a5536d8e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_g36XHCX86xnm9Fp8-ao39

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 metadata-completeness-analyzer-adjacent-row-changes-secondary-measure-a5536d8e -d '<json body>'
```

Example prompt: Can you run adjacent row change and weighted mean reports on this metadata completeness dataset? I have 4 rows — row-1 (group-a, 2026-10-01, present_fields=10, expected_fields=1), row-2 (group-b, 2026-10-02, present=20, expected=2), row-3 (group-a, 2026-10-03, present=15, expected=3), row-4 (group-b, 2026-10-04, present=30, expected=4) — and I want to analyze the present_fields column.

## When to prefer this

Choose this endpoint when you need dual analytical reports — adjacent row change detection and weighted mean — on metadata completeness records (present_fields, expected_fields) without requiring external data lookups or live predictions. Ideal for data quality pipelines, completeness audits, and trend analysis on structured row-level completeness data. Best when you have up to 200 rows and want two complementary statistical views in a single call at low cost.

## Known failure modes

- More than 200 rows submitted — endpoint rejects input exceeding maxItems
- Missing required fields (row_id, site_group, date, present_fields, expected_fields) — validation error
- Invalid date format (must match YYYY-MM-DD) — schema rejection
- row_id empty or exceeds 80 characters — validation error
- field enum value not 'present_fields' or 'expected_fields' — rejected
- Payment not included or insufficient (requires 0.01 USDC via x402) — 402 Payment Required

## How this service works

Metadata completeness — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean for supplied metadata completeness records (present_fields, expected_fields). 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) adjacent row changes showing how present_fields or expected_fields shift between consecutive rows, and (2) a secondary-measure weighted mean computed from the completeness records. Both reports are computed locally with no external lookups, covering up to 200 supplied rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/metadata-completeness-analyzer-adjacent-row-changes-secondary-measure-a5536d8e/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)