{"uid":"cap_Dy5xrXAQVcjpD-ly7AvWE","slug":"loot-observations-string-normalisation-numeric-row-ordering-ba2fde3d","name":"Loot Observations — String Normalisation + Numeric Row Ordering","description":"Loot observations — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied loot observations records (drop_count, attempt_count). 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-game-05-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","drop_count","attempt_count"],"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},"drop_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"attempt_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","game_area":"group-a","drop_count":10,"attempt_count":1},{"date":"2026-10-02","row_id":"row-2","game_area":"group-b","drop_count":20,"attempt_count":2},{"date":"2026-10-03","row_id":"row-3","game_area":"group-a","drop_count":15,"attempt_count":3},{"date":"2026-10-04","row_id":"row-4","game_area":"group-b","drop_count":30,"attempt_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["drop_count","attempt_count"],"type":"string","default":"drop_count"}}},"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__8H8uZUU7NpaxRNqZEBqA","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":"Normalises string fields and applies numeric row ordering to up to 200 loot observation records (drop_count, attempt_count), returning 2 analytical reports per call.","exampleAgentPrompt":"Clean up the string fields in my loot observation records and sort them numerically by drop_count — I have 4 rows: row-1 in group-a on 2026-10-01 with drop_count 10 and attempt_count 1, row-2 in group-b on 2026-10-02 with drop_count 20 and attempt_count 2, row-3 in group-a on 2026-10-03 with drop_count 15 and attempt_count 3, and row-4 in group-b on 2026-10-04 with drop_count 30 and attempt_count 4, and give me two reports.","exampleUseCases":[{"title":"Rank loot drops across game areas","prompt":"I've got 10 loot observation rows from my game with drop_count and attempt_count values across areas dungeon-east and dungeon-west from October 2026 — can you normalize the string fields and order everything by drop_count to see which areas had the most drops?"},{"title":"Clean attempt count data for game audit","prompt":"Sort and normalize my loot tracking records by attempt_count — I have 50 rows with game areas like boss-room and open-world spanning November 2026, and I need two clean analytical reports from the data."},{"title":"Pre-process loot data before pipeline ingestion","prompt":"Before I load this into my analytics pipeline, can you normalize the string columns and rank the rows numerically by drop_count? I have 150 loot observation records from December 2026 across multiple game zones."}],"resultDescription":"Two analytical reports based on the supplied loot observation rows: one covering string normalisation of fields such as game_area and date, and one covering numeric row ordering for the selected field (drop_count or attempt_count). Results are derived entirely from the submitted data with no external lookups.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, game_area, date, drop_count, attempt_count) on any row","Date field not matching YYYY-MM-DD pattern","Invalid field enum value (must be 'drop_count' or 'attempt_count')","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)","Empty rows array (minItems: 1 not met)"],"whenToPreferThis":"Choose this endpoint when you have gaming loot observation data (drop_count, attempt_count) and need both string field normalisation and numeric row ordering in a single call. It is best suited for small-to-medium batches up to 200 rows where you want two bundled analytical reports without any live data lookup or external enrichment. Prefer it over general-purpose data tools when working specifically with game loot telemetry and you want a low-cost ($0.01 USDC) self-contained local analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:34:50.162Z","isFirstParty":false,"canonicalSlug":"loot-observations-string-normalisation-numeric-row-ordering-ba2fde3d"}