{"uid":"cap_dV7vyFLVKbYnyfec_ZVFY","slug":"join-coverage-string-normalisation-and-adjacent-row-changes-analyzer-ae685bb0","name":"Join Coverage String Normalisation and Adjacent Row Changes Analyzer","description":"Join coverage — String normalisation + Adjacent row changes: String normalisation + 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-09-029?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_uvGmaqvS5QPP8pc0cqmdO","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 records by performing string normalization and detecting adjacent row changes across matched_rows and source_rows fields, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Analyze my join coverage records for string normalization issues and adjacent row changes — I have 4 rows for groups A and B dated 2026-10-01 through 2026-10-04 with matched_rows values of 10, 20, 15, 30 and source_rows of 1, 2, 3, 4. Run the analysis on the matched_rows field.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I have a week of join coverage data across two dataset groups — the matched_rows went 10, 20, 15, 30, 25 and source_rows went 1, 2, 3, 4, 5. Can you normalize the string labels and flag any sharp adjacent row changes in the matched_rows field so I can spot where the join quality dropped?"},{"title":"Detecting data drift between source and matched counts","prompt":"I'm reviewing daily join metrics for my data warehouse — here are 6 rows across groups 'alpha' and 'beta' with dates from 2026-11-01 to 2026-11-06. Run string normalization and detect adjacent row changes on the source_rows field so I can see where upstream volume shifted suddenly."},{"title":"Dataset group consistency check for reporting","prompt":"Before I publish my monthly join coverage report, can you run string normalization and adjacent row changes detection on these 8 rows across groups 'us-east', 'us-west', and 'eu-central', using the matched_rows field? Dates run from 2026-09-01 to 2026-09-08."}],"resultDescription":"Returns 2 analytical reports per call: (1) a string normalization report covering the dataset_group and row label fields, and (2) an adjacent row changes report highlighting step-changes between consecutive rows in the selected field (matched_rows or source_rows). Reports are generated from the supplied data with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","Numeric values outside allowed range (±1,000,000,000,000) — validation error","Empty rows array (minItems:1 not satisfied) — request rejected"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local analytical processing of join coverage tabular data — specifically string normalization of group labels and adjacent row change detection — without requiring external data sources, live lookups, or predictions. Ideal for ETL quality checks, data pipeline audits, and pre-report data validation on small to medium datasets (up to 200 rows).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:38.421Z","isFirstParty":false,"canonicalSlug":"join-coverage-string-normalisation-and-adjacent-row-changes-analyzer-ae685bb0"}