{"uid":"cap_SYK8lJXTYhtjzAsEM8dow","slug":"structured-data-observations-numeric-interval-filter-adjacent-row-fbfff5ff","name":"Structured Data Observations — Numeric Interval Filter + Adjacent Row Changes","description":"Structured data observations — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes for supplied structured data observations records (valid_items, total_items). 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-web-08-127?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","site_group","date","valid_items","total_items"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"site_group":{"type":"string","default":"","maxLength":80},"total_items":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"valid_items":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","site_group":"group-a","total_items":1,"valid_items":10},{"date":"2026-10-02","row_id":"row-2","site_group":"group-b","total_items":2,"valid_items":20},{"date":"2026-10-03","row_id":"row-3","site_group":"group-a","total_items":3,"valid_items":15},{"date":"2026-10-04","row_id":"row-4","site_group":"group-b","total_items":4,"valid_items":30}],"maxItems":200,"minItems":1},"field":{"enum":["valid_items","total_items"],"type":"string","default":"valid_items"},"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_Cs3bvtZF6jheB7TUJOot1","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":"Filters structured observation rows by a numeric interval on a chosen field and detects adjacent row changes, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a list of structured observation records with site_group, date, valid_items, and total_items — can you filter rows where valid_items is between 10 and 150 and also flag any adjacent rows where that value changed? I've got about 80 rows total.","exampleUseCases":[{"title":"QA check on daily site observation exports","prompt":"Here are 120 rows of daily site observation data with valid_items and total_items — filter to only rows where valid_items falls between 5 and 200, and also tell me wherever the value changed from one row to the next."},{"title":"Detecting anomalous sequential shifts in item counts","prompt":"I want to see which rows in my observation dataset have total_items in the range 0 to 1000, and also highlight any row where total_items jumped or dropped compared to the row immediately before it — there are about 60 records."},{"title":"Validating site group data before reporting","prompt":"Before I send this report, can you run two checks on my 45 observation rows: first filter valid_items to anything between 20 and 500, and second flag any adjacent row where valid_items changed so I can investigate those transitions?"}],"resultDescription":"Returns two reports: (1) rows from the input dataset whose chosen field (valid_items or total_items) falls within the specified [minimum, maximum] interval; (2) a list of adjacent row pairs where the chosen field value changed from one row to the next. Both reports operate on the same supplied dataset of up to 200 rows with no external lookups.","failureModes":["Empty or missing rows array returns an error","More than 200 rows submitted exceeds the per-call limit","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Minimum value greater than maximum value may produce an empty interval result","Null numeric values in valid_items or total_items may be excluded or cause unexpected behavior in comparisons","Unsupported field enum value returns a validation error"],"whenToPreferThis":"Choose this endpoint when you have structured observation data (with site_group, date, valid_items, total_items fields) and need both a numeric range filter AND adjacent-row change detection in a single low-cost call. It is ideal for lightweight data QA, pre-report validation, or sequential anomaly detection on datasets up to 200 rows, without needing external data sources or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:31:54.861Z","isFirstParty":false,"canonicalSlug":"structured-data-observations-numeric-interval-filter-adjacent-row-fbfff5ff"}