# Data Collection Completeness — Numeric Interval Filter + Adjacent Row Changes

> Data Collection Completeness — Numeric Interval Filter + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Runs two local analytical reports on completeness data: filters rows by a numeric interval and computes adjacent row changes for observed/expected values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-22-127?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-collection-completeness-numeric-interval-filter-adjacent-row-e2844766
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wdcit2EwCeDqbMTwdi_7-

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-numeric-interval-filter-adjacent-row-e2844766 -d '<json body>'
```

Example prompt: I have 50 rows of education data collection completeness records with observed_values and expected_values by course group and date — can you run two reports: filter rows where observed_values fall between 0 and 100, and compute adjacent row changes on observed_values?

## When to prefer this

Choose this endpoint when you need simultaneous interval filtering and adjacent-row change detection on completeness data (observed vs expected values) for education or data pipeline monitoring use cases, without any need for external lookups or predictions. Ideal for batch analytical workflows on up to 200 rows at low cost ($0.01 USDC for both reports).

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row-level validation errors
- Missing required fields (row_id, observed_values, expected_values) causes schema rejection
- Minimum value greater than maximum value produces empty interval filter results
- Non-numeric observed_values or expected_values causes type validation failure

## How this service works

Data collection completeness — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes 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

Returns two analytical reports for up to 200 rows: (1) a numeric interval filter identifying rows where the selected field (observed_values or expected_values) falls within the specified minimum/maximum range, and (2) an adjacent row changes report showing value differences between consecutive rows for the selected field.

## 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-collection-completeness-numeric-interval-filter-adjacent-row-e2844766/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)