# Matchmaking Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes

> Matchmaking Observations — Record Quality Audit + String Normalisation + 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 2026-11-02).

Runs three local analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 matchmaking observation rows containing queue_seconds and party_size fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-12-200?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/matchmaking-observations-record-quality-audit-string-normalisation-78e78186
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LwiqKSwPtJ5hkUKaVE2oH

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 matchmaking-observations-record-quality-audit-string-normalisation-78e78186 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and adjacent row changes analysis on my matchmaking data for the queue_seconds field? I have about 50 rows with row IDs, game areas like 'group-a' and 'group-b', dates in 2026, and queue time and party size values.

## When to prefer this

Choose this endpoint when you have matchmaking observation data (queue times and party sizes) and need three simultaneous analytical passes — quality auditing, string normalisation, and adjacent row change detection — in a single low-cost call. Prefer it over general-purpose data tools when your data is already structured in the expected schema and you want fast, local analysis without any external API calls or live predictions. It is especially useful as a pre-pipeline data validation step for gaming or matchmaking analytics workflows.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, game_area, date, queue_seconds, party_size) causes row-level rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Field selector value outside the allowed enum ('queue_seconds' or 'party_size') causes a request error
- Null or extreme numeric values (outside ±1,000,000,000,000) may trigger boundary flags in quality audit
- Empty rows array or fewer than 1 row returns an error

## How this service works

Matchmaking observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied matchmaking observations records (queue_seconds, party_size). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Returns three independent analytical reports: (1) a record quality audit identifying missing, null, or out-of-range values across rows; (2) a string normalisation report standardising game_area and other string fields; and (3) an adjacent row changes report highlighting value shifts between consecutive rows for the selected numeric field (queue_seconds or party_size). All analysis is performed 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",
     "game_area",
     "date",
     "queue_seconds",
     "party_size"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "game_area": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "party_size": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "queue_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "game_area": "group-a",
     "party_size": 1,
     "queue_seconds": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "game_area": "group-b",
     "party_size": 2,
     "queue_seconds": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "game_area": "group-a",
     "party_size": 3,
     "queue_seconds": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "game_area": "group-b",
     "party_size": 4,
     "queue_seconds": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "queue_seconds",
    "party_size"
   ],
   "type": "string",
   "default": "queue_seconds"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/matchmaking-observations-record-quality-audit-string-normalisation-78e78186/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)