{"uid":"cap_nfHBQbmnpEH7eDyOtkRQ-","slug":"texture-streaming-analytics-grouped-numeric-summary-numeric-interval-14ec53f6","name":"Texture Streaming Analytics — Grouped Numeric Summary + Numeric Interval Filter","description":"Texture streaming — Grouped numeric summary + Numeric interval filter: Grouped numeric summary + Numeric interval filter for supplied texture streaming records (loaded_mb, stream_ms). 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-09-088?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","loaded_mb","stream_ms"],"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},"loaded_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"stream_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","game_area":"group-a","loaded_mb":10,"stream_ms":1},{"date":"2026-10-02","row_id":"row-2","game_area":"group-b","loaded_mb":20,"stream_ms":2},{"date":"2026-10-03","row_id":"row-3","game_area":"group-a","loaded_mb":15,"stream_ms":3},{"date":"2026-10-04","row_id":"row-4","game_area":"group-b","loaded_mb":30,"stream_ms":4}],"maxItems":200,"minItems":1},"field":{"enum":["loaded_mb","stream_ms"],"type":"string","default":"loaded_mb"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_l0sjAlzBeMJNLrAFrrda_","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":"Computes grouped numeric summary statistics and applies a numeric interval filter on texture streaming records (loaded_mb, stream_ms) for up to 200 rows, returning 2 independent analytical reports.","exampleAgentPrompt":"I have texture streaming records for our game — can you group them by game_area and summarize the loaded_mb values, and also filter to only rows where loaded_mb is between 10 and 50? Here are the rows: row-1 (group-a, 2026-10-01, loaded_mb=10, stream_ms=1), row-2 (group-b, 2026-10-02, loaded_mb=20, stream_ms=2), row-3 (group-a, 2026-10-03, loaded_mb=15, stream_ms=3), row-4 (group-b, 2026-10-04, loaded_mb=30, stream_ms=4).","exampleUseCases":[{"title":"Game area texture load comparison","prompt":"Group these texture streaming records by game_area and give me a numeric summary of loaded_mb — I want to see which zones are pulling the most data. Records: row-1 (zone-alpha, 2026-11-01, loaded_mb=45, stream_ms=120), row-2 (zone-beta, 2026-11-01, loaded_mb=80, stream_ms=200), row-3 (zone-alpha, 2026-11-02, loaded_mb=50, stream_ms=130). Use loaded_mb as the field, minimum 0, maximum 100."},{"title":"Stream latency outlier filtering","prompt":"Filter my texture streaming rows to only those with stream_ms between 100 and 500, and also summarize stream_ms grouped by game_area. Data: row-1 (area-1, 2026-09-10, loaded_mb=12, stream_ms=80), row-2 (area-2, 2026-09-11, loaded_mb=22, stream_ms=300), row-3 (area-1, 2026-09-12, loaded_mb=18, stream_ms=450), row-4 (area-2, 2026-09-13, loaded_mb=35, stream_ms=600). Field is stream_ms, min 100, max 500."},{"title":"Daily texture load range analysis","prompt":"I need a grouped summary and interval filter for texture streaming data from our open-world game. Rows: row-1 (desert, 2026-12-01, loaded_mb=200, stream_ms=500), row-2 (forest, 2026-12-01, loaded_mb=350, stream_ms=800), row-3 (desert, 2026-12-02, loaded_mb=180, stream_ms=450), row-4 (forest, 2026-12-02, loaded_mb=400, stream_ms=900). Analyze loaded_mb, filter between 150 and 380."}],"resultDescription":"Returns two analytical reports: (1) a grouped numeric summary breaking down statistics (e.g. count, mean, min, max) for the selected metric (loaded_mb or stream_ms) segmented by game_area, and (2) a filtered subset of records where the chosen field falls within the specified [minimum, maximum] interval. Both reports are computed locally without external data lookups.","failureModes":["Empty or missing rows array returns an error","More than 200 rows submitted exceeds the limit and is rejected","Invalid date format (not YYYY-MM-DD) causes a validation error","minimum greater than maximum causes an empty or invalid interval filter result","Unrecognized field value (not loaded_mb or stream_ms) returns a schema validation error","Null values in loaded_mb or stream_ms may be excluded from numeric summaries"],"whenToPreferThis":"Choose this endpoint when you have up to 200 texture streaming records with loaded_mb and stream_ms fields and need both a grouped category summary and a numeric range filter in a single call. It is ideal for game performance teams analyzing per-area texture loading behavior or filtering streaming anomalies by value range without needing external data enrichment, predictions, or real-time lookups.","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":"texture-streaming-analytics-grouped-numeric-summary-numeric-interval-14ec53f6"}