{"uid":"cap_LwiqKSwPtJ5hkUKaVE2oH","slug":"matchmaking-observations-record-quality-audit-string-normalisation-78e78186","name":"Matchmaking Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-game-12-200?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"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"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_dSOfK1gyciHY6jPCE4qhz","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"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.","exampleUseCases":[{"title":"Auditing dirty matchmaking queue data","prompt":"I have 80 rows of matchmaking observations from last month — each has a row ID, game area, date, queue_seconds, and party_size. Can you run a full record quality audit, string normalisation, and adjacent row changes check on the queue_seconds field so I can see where the data is messy?"},{"title":"Detecting sudden party size shifts between rounds","prompt":"I've got a dataset of 120 matchmaking events with party sizes and queue times across different game areas. Can you analyse the party_size field for adjacent row changes and also give me the quality audit and string normalisation reports?"},{"title":"Pre-pipeline data cleaning for matchmaking logs","prompt":"Before I load our matchmaking logs into the data warehouse, can you run string normalisation, record quality audit, and adjacent row changes on these 150 rows? The field I care most about is queue_seconds."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:31:50.833Z","isFirstParty":false,"canonicalSlug":"matchmaking-observations-record-quality-audit-string-normalisation-78e78186"}