{"uid":"cap_fnxvyZ5jyYw6N6kRgb0_O","slug":"record-routing-numeric-interval-filter-adjacent-row-changes-7232988c","name":"Record Routing: Numeric Interval Filter + Adjacent Row Changes","description":"Record routing — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes for supplied record routing records (accepted_records, rejected_records). 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-analytics-19-127?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","accepted_records","rejected_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"accepted_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"rejected_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","accepted_records":10,"rejected_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","accepted_records":20,"rejected_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","accepted_records":15,"rejected_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","accepted_records":30,"rejected_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["accepted_records","rejected_records"],"type":"string","default":"accepted_records"},"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_S82kixnQnHNh_RbxQUk4c","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":"Analyzes record routing data (accepted/rejected records) by applying a numeric interval filter and detecting adjacent row changes, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a routing dataset with accepted and rejected record counts — can you filter it to rows where accepted_records falls between 5 and 100, and also flag any adjacent rows where the value changed? Here's the data: row-1 2026-10-01 group-a accepted=10 rejected=1, row-2 2026-10-02 group-b accepted=20 rejected=2, row-3 2026-10-03 group-a accepted=150 rejected=3.","exampleUseCases":[{"title":"Pipeline acceptance anomaly detection","prompt":"I have daily routing logs with accepted and rejected record counts per group. Can you filter to rows where accepted_records is between 0 and 50, and also flag every place where the count changed from one row to the next? My data is: row-1 2026-10-01 group-a accepted=10 rejected=1, row-2 2026-10-02 group-b accepted=55 rejected=2, row-3 2026-10-03 group-a accepted=30 rejected=0."},{"title":"Rejected record threshold audit","prompt":"Run two reports on my routing data using rejected_records as the field: first, filter to rows where rejected_records falls between 1 and 10, and second, show me wherever the rejected count shifted between consecutive rows. Data: row-1 2026-11-01 group-x accepted=100 rejected=3, row-2 2026-11-02 group-x accepted=120 rejected=12, row-3 2026-11-03 group-x accepted=90 rejected=5."},{"title":"Group-level routing quality review","prompt":"I want to check my data routing quality for groups A and B — filter rows where accepted_records is between 15 and 200, and flag adjacent row changes in accepted_records. Here's the dataset: row-1 2026-12-01 group-a accepted=20 rejected=2, row-2 2026-12-02 group-a accepted=200 rejected=1, row-3 2026-12-03 group-b accepted=10 rejected=4, row-4 2026-12-04 group-b accepted=80 rejected=0."}],"resultDescription":"Returns two reports: (1) rows where the selected field (accepted_records or rejected_records) falls within the specified minimum–maximum interval, and (2) a row-by-row adjacency change report showing where consecutive rows differ in value. Both reports are derived solely from the supplied data using local analytical engines — no external lookups or predictions.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Field value not in enum ['accepted_records','rejected_records'] — validation error","minimum greater than maximum — may return empty interval filter result","null values in accepted_records or rejected_records — affected rows may be excluded from computations"],"whenToPreferThis":"Choose this endpoint when you have structured record routing data with accepted/rejected record counts and need two specific analyses in one call: a numeric range filter and adjacent-row change detection. It is ideal for ETL pipeline audits, data quality checks, and routing anomaly detection where you already have the data in hand and want fast, local computation without any external API calls or live data lookups. It covers up to 200 rows per call for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:53:36.620Z","isFirstParty":false,"canonicalSlug":"record-routing-numeric-interval-filter-adjacent-row-changes-7232988c"}