{"uid":"cap_siTjqHPpLNPFBGmrirUiV","slug":"brand-mention-inventories-string-normalisation-equal-width-histogram-e26988f9","name":"Brand Mention Inventories — String Normalisation + Equal-Width Histogram","description":"Brand mention inventories — String normalisation + Equal-width histogram: String normalisation + Equal-width histogram 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-18-031?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"bins":{"type":"integer","default":5,"maximum":30,"minimum":1},"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_jehVJ56Kzd6uyWJFuufSi","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":"Runs string normalisation and equal-width histogram analysis on brand mention inventory records (mention_count, source_count) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Take my brand mention inventory data — 150 rows with mention_count and source_count fields for multiple company groups — and run string normalisation plus an equal-width histogram on mention_count using 10 bins, so I can see the frequency distribution across the dataset.","exampleUseCases":[{"title":"Distribution analysis of brand mentions","prompt":"I have 120 rows of brand mention records across three company groups with dates and mention counts — can you run an equal-width histogram with 8 bins on the mention_count field so I can see how mentions are distributed?"},{"title":"Source count normalisation for multi-brand report","prompt":"I need to normalise the source_count values in my brand inventory dataset of 80 rows and get a histogram breakdown using 5 bins — the field I want to analyse is source_count."},{"title":"Competitive brand monitoring frequency audit","prompt":"We track brand mentions for 5 company groups daily over the past quarter — I have about 180 rows. Run string normalisation and a 10-bin equal-width histogram on mention_count so I can audit which frequency ranges dominate."}],"resultDescription":"Two analytical reports: a string normalisation report (cleaned/standardised values for the supplied records) and an equal-width histogram report (bin boundaries, frequencies, and counts for the chosen field — mention_count or source_count) computed locally against up to 200 input rows.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (non YYYY-MM-DD) causes row validation failure","bins value outside 1–30 range causes schema validation error","Missing required row fields (row_id, date, mention_count, source_count, company_group) causes rejection","Null numeric values may affect histogram bin computation","Payment of 0.01 USDC not completed causes 402 response"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis — specifically string normalisation and equal-width histogram binning — on brand mention inventory data without any live data lookup, prediction, or external API calls. It is ideal for small to medium datasets (up to 200 rows) where you want reproducible, deterministic analytics at minimal cost ($0.01 USDC for two reports). Prefer alternatives if you need real-time brand data fetching, sentiment analysis, or predictive modelling.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:33.747Z","isFirstParty":false,"canonicalSlug":"brand-mention-inventories-string-normalisation-equal-width-histogram-e26988f9"}