{"uid":"cap_qiuweidD5d9qnWiMJ7q-K","slug":"join-coverage-analytics-adjacent-row-changes-base-100-series-cc70bb4e","name":"Join Coverage Analytics: Adjacent Row Changes + Base-100 Series","description":"Join coverage — Adjacent row changes + Base-100 series: Adjacent row changes + Base-100 series 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-09-155?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_n8uQnTJU2DLZOWT1wxI_H","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 adjacent row-over-row changes and a base-100 indexed series for join coverage metrics (matched_rows or source_rows) across up to 200 supplied records.","exampleAgentPrompt":"Run adjacent row change and base-100 series analysis on my join coverage data — I have matched_rows and source_rows per date from 2026-10-01 to 2026-10-04 across groups group-a and group-b, analyze the matched_rows field starting from base index 0.","exampleUseCases":[{"title":"ETL pipeline join rate trend","prompt":"I have daily join coverage records for my ETL pipeline — each row has a date, matched_rows, source_rows, and a dataset group. Can you calculate the adjacent row changes and a base-100 trend series for matched_rows, using the first row as the base?"},{"title":"Dataset group comparison over time","prompt":"Track how our join coverage has changed day over day for these two dataset groups. Give me both the row-to-row differences and an indexed series normalized to 100 at row 0 for source_rows."},{"title":"Join quality regression detection","prompt":"I need to detect regressions in our join quality — run adjacent row change analysis and a base-100 series on matched_rows for these 50 coverage records across our group-a and group-b datasets, starting the base index at row 0."}],"resultDescription":"Returns two analytical reports: (1) adjacent row-over-row changes for the selected metric (matched_rows or source_rows) across the supplied records, and (2) a base-100 indexed series anchored at the specified base_index row. Both reports cover up to 200 rows and are computed locally without external lookups.","failureModes":["More than 200 rows submitted — API rejects with validation error","Missing required fields (row_id, date, matched_rows, source_rows, dataset_group) causes schema validation failure","Invalid date format (not YYYY-MM-DD) results in rejection","base_index out of range (0–199) causes error","Null values in matched_rows or source_rows may affect computation output","Payment failure via x402 prevents execution"],"whenToPreferThis":"Choose this endpoint when you have pre-collected join coverage records (matched_rows and source_rows) and need both a row-over-row delta analysis and a base-indexed normalization series in a single low-cost call. Ideal for data engineers and analysts monitoring ETL join quality over time who want lightweight, offline analytical computation without spinning up a full analytics stack.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:18.797Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-adjacent-row-changes-base-100-series-cc70bb4e"}