{"uid":"cap_L2xx-yMDCU3eStymPWooZ","slug":"numora-statistics-histogram-b0524435","name":"Numora Statistics Histogram","description":"100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.","url":"https://numormor.netlify.app/api/statistics/histogram","method":"POST","headers":{},"bodySchema":{"type":"object","required":["data"],"properties":{"bins":{"type":"integer"},"data":{"type":"array"}}},"responseSchema":{"type":"object","required":["success","result","computation"],"properties":{"result":{"type":"object","description":"Computation result varies by endpoint"},"success":{"type":"boolean","description":"Always true on success"},"computation":{"type":"string","description":"Human-readable description of what was computed"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","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":{"p10Cents":"10.0000","medianCents":"10.0000","p90Cents":"10.0000","minCents":"10.0000","maxCents":"10.0000","p95Cents":"10.0000","sampleCount":1,"varies":false,"failureChargeRate":1},"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_3rz0zrv3TurSUOfT98cBj","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes a frequency histogram from a numeric data array, returning bin edges and counts for distribution analysis","exampleAgentPrompt":"Can you compute a histogram with 10 bins for this dataset: [3.2, 5.1, 2.8, 7.4, 6.0, 1.9, 4.5, 8.3, 5.5, 3.7, 6.8, 2.1, 7.0, 4.9, 5.3]?","exampleUseCases":[{"title":"Analyze customer purchase amount distribution","prompt":"I have a list of customer order totals from last month. Can you create a histogram with 8 bins to show me how the purchase amounts are distributed? Here's the data: [24.50, 156.30, 89.20, 45.75, 312.10, 67.40, 198.60, 52.30, 401.20, 75.85, 129.45, 38.90, 267.15, 94.60, 183.25, 55.40, 340.70, 112.30]"},{"title":"Assess website load time performance patterns","prompt":"We tracked our API response times over the past week in milliseconds. Can you bin these response times into 12 frequency bins so we can see what performance ranges most requests fall into? Times: [145, 267, 89, 412, 198, 156, 523, 178, 245, 312, 167, 489, 201, 334, 156, 278, 445, 212, 189, 356, 234, 401, 167, 289]"},{"title":"Understand student test score spread","prompt":"I need to analyze how our class did on the final exam to identify if we have a bimodal distribution. Can you compute a histogram with 15 bins from these scores: [45, 62, 78, 85, 51, 72, 88, 95, 48, 71, 82, 91, 56, 68, 84, 89, 52, 75, 87, 93, 61, 79, 86, 94, 49, 73, 81, 90, 58, 77]?"}],"resultDescription":"Returns a JSON object with success=true, a computation string describing what was calculated, and a result object containing histogram bin edges and the count of data points falling within each bin, representing the full frequency distribution of the input array.","failureModes":["Empty data array returns an error — data must have at least one element","Non-numeric values in the data array may cause a computation failure","Bins value of zero or negative integer may return an error or default behavior","Very large datasets may exceed payload limits","Payment failure via x402 returns a 402 status before computation runs"],"whenToPreferThis":"Choose this endpoint when you need a pure server-side histogram computation with no external dependencies — ideal for AI agents that need fast, deterministic frequency distribution analysis over a numeric array without spinning up a Python/NumPy environment. Prefer it over general-purpose code execution APIs when you want a lightweight, single-purpose math call with micropayment billing.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T00:56:43.013Z","isFirstParty":false}