{"uid":"cap_wVYbLYoNKZHiPpwgYFV_5","slug":"brand-mention-inventories-string-normalisation-exact-row-deduplication-4fff035a","name":"Brand Mention Inventories — String Normalisation + Exact Row Deduplication","description":"Brand mention inventories — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied brand mention inventories records (mention_count, source_count). 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-company-18-020?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","mention_count","source_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"mention_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_count":1,"company_group":"group-a","mention_count":10},{"date":"2026-10-02","row_id":"row-2","source_count":2,"company_group":"group-b","mention_count":20},{"date":"2026-10-03","row_id":"row-3","source_count":3,"company_group":"group-a","mention_count":15},{"date":"2026-10-04","row_id":"row-4","source_count":4,"company_group":"group-b","mention_count":30}],"maxItems":200,"minItems":1}}},"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_n1MdEJIle3sUd2yC_ZDCI","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 removes exact duplicate rows from brand mention inventory records (mention_count, source_count), producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Clean up my brand mention inventory by normalising company name strings and removing exact duplicate rows — here are my records for groups 'Acme Corp' and 'Beta Ltd' across dates 2026-10-01 to 2026-10-04, with mention_count and source_count values for each row. I need 2 reports back.","exampleUseCases":[{"title":"Deduplicating weekly brand mention exports","prompt":"I've got a weekly export of brand mention data for 'NovaBrand' and 'AlphaCo' across October — there are duplicate rows and inconsistent company name strings. Can you normalise the strings and strip out exact duplicate rows? The dataset has mention_count and source_count columns and about 150 rows total."},{"title":"Cleaning multi-source brand tracking data","prompt":"We pull brand mention counts from multiple sources and end up with duplicated rows and messy company group names. Can you run string normalisation and exact deduplication on this batch of up to 200 rows and give me two clean analytical reports?"},{"title":"Pre-analysis data quality check on mention inventory","prompt":"Before I hand this brand mention inventory off to the analytics team, I want to normalise all the company_group strings and remove any exact duplicate rows. There are about 80 rows covering groups 'CorpX' and 'CorpY' with their mention_count and source_count values — can you generate the two deduplication reports?"}],"resultDescription":"Two analytical reports covering: (1) string-normalised versions of the supplied brand mention inventory rows, and (2) an exact-row-deduplicated dataset — both derived from the submitted records without any external lookups or predictions. Up to 200 input rows are supported per call.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, company_group, date, mention_count, source_count) cause validation errors","Date fields not matching YYYY-MM-DD pattern will fail schema validation","Numeric fields outside the allowed range (±1,000,000,000,000) are rejected","Empty rows array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you have brand mention inventory data (with mention_count and source_count fields per company group and date) that needs string normalisation and exact row deduplication in a single low-cost call ($0.01 USDC for 2 reports, up to 200 rows). It is a purely local analytical operation with no live lookups, making it fast and deterministic. Prefer alternatives if you need outlier detection, time-series analysis, pivot tables, or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-04T12:40:20.909Z","isFirstParty":false,"canonicalSlug":"brand-mention-inventories-string-normalisation-exact-row-deduplication-4fff035a"}