{"uid":"cap_ZABEJf5l9ESliglc22jIK","slug":"numeric-precision-comparisons-string-normalisation-keyed-dataset-cfa192b3","name":"Numeric Precision Comparisons — String Normalisation + Keyed Dataset Comparison","description":"Numeric precision comparisons — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied numeric precision comparisons records (source_value, transformed_value). 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-20-036?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"other":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":21,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":17,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":33,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":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_hLexgsyOlTPlRYMKz-pdN","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":"Compares two keyed datasets of up to 200 rows each, normalising strings and analysing numeric precision differences between source and transformed values, producing two independent analytical reports.","exampleAgentPrompt":"Can you compare these two datasets for me — I have 150 rows with row_id, dataset_group, date, source_value, and transformed_value in each set — and give me a string normalisation report plus a keyed comparison showing where the numeric values differ between the original and transformed versions?","exampleUseCases":[{"title":"ETL pipeline numeric drift audit","prompt":"I ran our sales figures through a currency conversion pipeline and I want to verify the output didn't introduce rounding errors. Here are the original values and the pipeline output as two datasets of about 80 rows each, keyed by transaction_id and date — can you compare them and flag any numeric precision issues?"},{"title":"A/B data transformation validation","prompt":"We have two versions of a metric calculation — version A is the source and version B is the transformed result. I've got 120 rows grouped into 'control' and 'experiment' dataset groups. Can you normalise the string keys and compare the numeric values row by row to see where they diverge?"},{"title":"Financial reconciliation between systems","prompt":"I need to reconcile figures between our accounting system export and our data warehouse — 60 rows per dataset, each with a row_id, a date, a source_value from the ledger, and a transformed_value from the warehouse. Can you run a keyed comparison and string normalisation on these two datasets and tell me where they don't match?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report detailing how string keys and group identifiers were normalised across both datasets, and (2) a keyed dataset comparison report showing row-by-row numeric precision differences between source_value and transformed_value fields, with summaries by dataset_group. No external lookups or live data are involved — all analysis is performed on the supplied rows.","failureModes":["More than 200 rows supplied — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause row-level validation errors","Date strings not matching YYYY-MM-DD pattern will fail schema validation","Numeric values outside the ±1,000,000,000,000 range are rejected","Empty rows array (minItems: 1 not met) returns a schema validation error","Payment of 0.01 USDC not completed results in 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you need to compare two structured numeric datasets row-by-row for precision drift or transformation accuracy, especially when data is already available locally and no external lookup, live data feed, or prediction is required. It is well-suited for ETL validation, reconciliation between two system exports, or auditing numeric transformations across up to 200 keyed rows. The flat per-call cost covers both reports simultaneously, making it efficient for dual-report needs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:48.600Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-keyed-dataset-cfa192b3"}