{"uid":"cap_fkfCQRJccgvXHizm2wjVy","slug":"facilities-usage-string-normalisation-and-keyed-dataset-comparison-b48a0ddd","name":"Facilities Usage String Normalisation and Keyed Dataset Comparison","description":"Facilities usage — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied facilities usage records (occupied_seats, available_seats). 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-operations-12-036?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","occupied_seats","available_seats"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"occupied_seats":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"available_seats":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","occupied_seats":10,"available_seats":1},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","occupied_seats":20,"available_seats":2},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","occupied_seats":15,"available_seats":3},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","occupied_seats":30,"available_seats":4}],"maxItems":200,"minItems":1},"other":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","occupied_seats","available_seats"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"occupied_seats":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"available_seats":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","occupied_seats":10,"available_seats":1},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","occupied_seats":21,"available_seats":2},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","occupied_seats":17,"available_seats":3},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","occupied_seats":33,"available_seats":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_1_eDI8IINuNXCxila3ywA","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":"Normalises strings and compares two facilities usage datasets (occupied_seats, available_seats) by key, returning 2 analytical reports for up to 200 rows each.","exampleAgentPrompt":"I have two batches of facilities usage records — each with up to 200 rows containing row_id, team_group, date, occupied_seats, and available_seats — can you normalize the team group strings and compare the two datasets row-by-row to flag any seat count discrepancies?","exampleUseCases":[{"title":"Reconciling two weekly occupancy reports","prompt":"I've got two weekly facilities reports for October with row IDs, team groups, dates, occupied seats, and available seats — one from the booking system and one from the facilities team. Can you normalize the team names and compare them row-by-row to show me where the seat counts differ?"},{"title":"Auditing seat utilization across team groups","prompt":"Can you take these two sets of facilities usage records — up to 200 rows each, with occupied and available seat counts per team group and date — normalize any inconsistent group name formatting, and give me a keyed comparison showing which rows have mismatched numbers?"},{"title":"Validating data consistency before dashboard merge","prompt":"Before I merge these two occupancy data exports into our dashboard, can you run a string normalization and keyed comparison on them? Each dataset has row_id, team_group, date, occupied_seats, and available_seats fields, and I need to catch any discrepancies between the two sources."}],"resultDescription":"Returns 2 analytical reports: (1) a string normalisation report standardising team_group and other string fields across the supplied rows, and (2) a keyed dataset comparison report identifying row-level differences in occupied_seats and available_seats between the two supplied arrays, matched by row_id. No external data is fetched; all analysis is performed locally on the supplied records.","failureModes":["Input arrays exceed 200 rows — request will be rejected or truncated","Missing required fields (row_id, team_group, date, occupied_seats, available_seats) cause row-level errors","Date strings not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside allowed range cause validation errors","Empty input array (minItems:1 not met) returns error","Mismatched row_ids between the two arrays result in unmatched rows in comparison report"],"whenToPreferThis":"Choose this endpoint when you have two facilities usage datasets (with occupied_seats and available_seats) that you need to both clean (string normalisation of team/group names) and reconcile (keyed row-by-row comparison) in a single low-cost call. It is ideal for up to 200 rows per dataset where no live data lookup, prediction, or external action is needed — purely local analytical processing on supplied data at $0.01 per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:49:52.602Z","isFirstParty":false,"canonicalSlug":"facilities-usage-string-normalisation-and-keyed-dataset-comparison-b48a0ddd"}