{"uid":"cap_XczYWTnrKbl8iNbpJ7xJb","slug":"brand-mention-inventories-string-normalisation-daily-sum-series-d6c91489","name":"Brand Mention Inventories — String Normalisation + Daily Sum Series","description":"Brand mention inventories — String normalisation + Daily sum series: String normalisation + Daily sum series 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-18-027?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},"field":{"enum":["mention_count","source_count"],"type":"string","default":"mention_count"}}},"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_eGiJmD26Rb46IfC1hVFIo","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 brand mention strings and computes daily sum time series for supplied brand mention inventory records (mention_count, source_count) across up to 200 rows.","exampleAgentPrompt":"Normalise the company group strings and compute a daily sum series for mention_count across my brand mention rows — I have about 50 rows with fields row_id, company_group, date, mention_count, and source_count.","exampleUseCases":[{"title":"Daily brand mention rollup by group","prompt":"I have 120 rows of brand mention inventory data with company_group, date, mention_count, and source_count — can you normalise the strings and give me a daily sum series for mention_count so I can see totals per day?"},{"title":"Source count aggregation for media audit","prompt":"Run the string normalisation and daily sum series on my brand mention records using source_count as the field — I've got about 80 rows covering two company groups across October."},{"title":"Standardise messy company names in mention data","prompt":"My brand mention export has inconsistent company_group labels and I need both string normalisation and a daily sum series for mention_count — can you process all 200 rows and give me the two reports?"}],"resultDescription":"Two analytical reports derived from the supplied rows: (1) a string normalisation report cleaning company_group and related text fields, and (2) a daily sum time series for the selected metric (mention_count or source_count) aggregated across all company groups by date. No external data is fetched; computation is performed entirely on the submitted rows.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — exceeds limit, request rejected","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required fields (row_id, company_group, date, mention_count, source_count) — rejected","Numeric values outside allowed range — schema validation failure","Invalid field enum value (not mention_count or source_count) — schema error"],"whenToPreferThis":"Use this endpoint when you have pre-collected brand mention inventory records and need two lightweight offline analytical operations — string normalisation and daily sum aggregation — applied together in a single call for a flat fee. It is ideal for batch post-processing of scraped or exported mention data without needing a live lookup service. Prefer this over heavier analytics pipelines when your dataset is under 200 rows and you only need these two specific report types.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:51.058Z","isFirstParty":false,"canonicalSlug":"brand-mention-inventories-string-normalisation-daily-sum-series-d6c91489"}