# Data Collection Completeness — Exact Row Deduplication + Numeric Distribution

> Data Collection Completeness — Exact Row Deduplication + Numeric Distribution 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).

Runs two local analytical reports on education data collection completeness records: exact row deduplication and numeric distribution analysis for observed vs. expected values, across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-22-039?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/data-collection-completeness-exact-row-deduplication-numeric-339ed481
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_mrHrauFYUYphgBmtVGEI9

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 data-collection-completeness-exact-row-deduplication-numeric-339ed481 -d '<json body>'
```

Example prompt: Can you run deduplication and numeric distribution analysis on my education data completeness records? I have rows with course_group, date, observed_values, and expected_values — I want to analyze the observed_values field across up to 50 rows to spot duplicates and understand the value spread.

## When to prefer this

Choose this endpoint when you need lightweight, local deduplication and numeric distribution analysis specifically on education data completeness records (observed vs. expected values) without any external data lookups or predictions. Ideal for small-to-medium batches (up to 200 rows) where you want two bundled analytical reports at low cost ($0.01 USDC). Prefer this over general-purpose analytics when your data is already structured with course_group, date, and observed/expected value fields and you want a quick completeness audit.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_values, expected_values) causes schema rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is not accepted (minItems: 1)
- Invalid field enum value (anything other than observed_values or expected_values) causes error

## How this service works

Data collection completeness — Exact row deduplication + Numeric distribution: Exact row deduplication + Numeric distribution for supplied data collection completeness records (observed_values, expected_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 analytical reports: (1) an exact row deduplication report identifying duplicate records in the submitted dataset, and (2) a numeric distribution summary for the selected field (observed_values or expected_values), covering statistics such as frequency, spread, and value ranges — all computed locally on the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-collection-completeness-exact-row-deduplication-numeric-339ed481/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)