{"uid":"cap_OWYanrEUGlgKuPCVFbcF6","slug":"join-coverage-duplicate-key-report-adjacent-row-changes-5ca90d5d","name":"Join Coverage Duplicate Key Report & Adjacent Row Changes","description":"Join coverage — Duplicate key report + Adjacent row changes: Duplicate key report + Adjacent row changes for supplied join coverage records (matched_rows, source_rows). 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-09-062?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"}}},"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_8ojD4qr-MdvXi40K1bnQ8","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 join coverage data rows to produce two reports: a duplicate key report and an adjacent row changes report, for up to 200 supplied rows.","exampleAgentPrompt":"Can you run a duplicate key report and adjacent row changes analysis on my join coverage data? I have 4 rows — row-1 through row-4 dated 2026-10-01 to 2026-10-04 with matched_rows values of 10, 20, 15, 30 and source_rows of 1, 2, 3, 4, split across group-a and group-b — analyze the matched_rows field.","exampleUseCases":[{"title":"ETL pipeline join audit","prompt":"I've got join coverage output from my ETL pipeline with 80 rows across two dataset groups — can you check for duplicate row keys and flag where matched_rows jumps significantly between consecutive rows?"},{"title":"Data quality check on source rows","prompt":"Run a duplicate key report and adjacent row changes check on my join coverage records using the source_rows field — I have about 150 rows tagged by date and dataset group and I need to catch any anomalies before we load to production."},{"title":"Regression test for join output","prompt":"After our latest data transformation, I want to verify the join coverage output — please generate a duplicate key report and an adjacent row changes report for these 50 rows across group-a and group-b, analyzing the matched_rows column."}],"resultDescription":"Two independent analytical reports returned together: (1) a duplicate key report identifying any row_ids that appear more than once across the supplied rows, and (2) an adjacent row changes report showing value differences between consecutive rows for the selected field (matched_rows or source_rows). Both reports are computed locally from the supplied data, covering up to 200 rows.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes rejection","Malformed date strings not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside the allowed range (-1000000000000 to 1000000000000) are rejected","Supplying fewer than 1 row returns a validation error","Invalid field enum value (anything other than matched_rows or source_rows) fails validation"],"whenToPreferThis":"Choose this endpoint when you need both a duplicate key audit and an adjacent row change analysis in a single call for join coverage data. It is ideal for data engineers validating ETL join outputs, QA workflows checking data integrity before loading, or regression testing join pipelines. It is best when you have up to 200 rows and want two complementary diagnostic reports without making separate API calls.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:30.540Z","isFirstParty":false,"canonicalSlug":"join-coverage-duplicate-key-report-adjacent-row-changes-5ca90d5d"}