{"uid":"cap_vpB-3Qoi_ZzUTu5_BQB5M","slug":"geospatial-tile-processing-numeric-row-ordering-secondary-measure-52b62a27","name":"Geospatial Tile Processing — Numeric Row Ordering + Secondary-Measure Weighted Mean","description":"Geospatial tile processing — Numeric row ordering + Secondary-measure weighted mean: Numeric row ordering + Secondary-measure weighted mean for supplied geospatial tile processing records (tile_count, processing_seconds). 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-weather-21-121?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","tile_count","processing_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tile_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"processing_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","tile_count":10,"station_group":"group-a","processing_seconds":1},{"date":"2026-10-02","row_id":"row-2","tile_count":20,"station_group":"group-b","processing_seconds":2},{"date":"2026-10-03","row_id":"row-3","tile_count":15,"station_group":"group-a","processing_seconds":3},{"date":"2026-10-04","row_id":"row-4","tile_count":30,"station_group":"group-b","processing_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["tile_count","processing_seconds"],"type":"string","default":"tile_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_bctOa3WMTG3Bo2z6uRXNZ","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":"Ranks geospatial tile processing records by numeric row order and computes a secondary-measure weighted mean across up to 200 supplied rows, returning two analytical reports.","exampleAgentPrompt":"I have a batch of geospatial tile processing records — up to 50 rows with fields like row_id, station_group, date, tile_count, and processing_seconds — can you rank them numerically by row order and compute a weighted mean on tile_count, returning both reports?","exampleUseCases":[{"title":"Station group tile throughput analysis","prompt":"I have 80 rows of daily tile processing logs across station groups A, B, and C, each with a tile_count and processing_seconds value — can you rank them by row order and give me a weighted mean on processing_seconds so I can see which groups are slower?"},{"title":"Geospatial pipeline performance audit","prompt":"Here are 120 geospatial tile processing records from October 2026, with row IDs, dates, and both tile_count and processing_seconds — please run the numeric row ordering and weighted mean report on tile_count for me."},{"title":"Daily tile count weighted summary","prompt":"I've got 30 tile processing entries from our rendering pipeline, each with a station group, date, tile_count, and processing_seconds — rank them numerically and compute the secondary-measure weighted mean on processing_seconds so I can spot outliers."}],"resultDescription":"Returns two analytical reports: (1) the input rows sorted by numeric row order, and (2) a secondary-measure weighted mean computed over the chosen field (tile_count or processing_seconds), covering up to 200 input rows per call.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, station_group, date, tile_count, processing_seconds) — row-level validation failure","Invalid date format (not YYYY-MM-DD) — pattern validation error","Numeric values outside allowed range (±1,000,000,000,000) — schema validation error","Empty rows array — minItems violation","Invalid field selector (not 'tile_count' or 'processing_seconds') — enum validation error"],"whenToPreferThis":"Choose this endpoint when you have structured geospatial tile processing tabular data (up to 200 rows) and need both a numeric row ranking and a weighted mean on a numeric measure in a single low-cost call. It is a pure local analytical computation with no external lookups, making it deterministic and fast. Prefer it over general-purpose analytics tools when the data fits the tile_count/processing_seconds schema and you want two bundled reports for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:47:39.639Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-numeric-row-ordering-secondary-measure-52b62a27"}