{"uid":"cap_QWMIutoJhWgbUe1V-yQrX","slug":"24k-labs-stats-toolkit-bc05c263","name":"24K Labs Stats Toolkit","description":"One call for mean/median/mode, stddev/variance, quartiles/percentiles, z-scores, IQR and z-score outlier flags; optional Pearson correlation and linear regression against a second series.","url":"https://api.24klabs.ai/api/v1/stats-toolkit","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"data":{"type":"array"}}},"responseSchema":{"type":"json","example":{"mean":5,"stdev":2,"median":4.5,"outliers":[]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","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":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_e_bblpci7-sxPmIaWj68w","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes descriptive statistics (mean, median, mode, stddev, variance, quartiles, percentiles, z-scores, IQR), detects outliers, and optionally runs Pearson correlation and linear regression against a second data series.","exampleAgentPrompt":"Can you run full descriptive stats on this list of values — [12, 45, 7, 89, 34, 56, 23, 67, 11, 90] — including mean, median, stddev, quartiles, z-scores, and flag any outliers? Also run a Pearson correlation and linear regression against this second series: [10, 42, 5, 85, 30, 50, 20, 60, 9, 88].","exampleUseCases":[{"title":"Exploratory data analysis on sales figures","prompt":"I have monthly sales numbers for the past year: [4200, 3800, 5100, 4750, 6200, 5900, 4100, 3600, 7000, 5500, 4800, 6100]. Can you give me mean, median, stddev, quartiles, and flag any outlier months using z-scores?"},{"title":"Correlation between ad spend and revenue","prompt":"I want to know how strongly correlated my ad spend is with my revenue. Here are the two series — ad spend: [1000, 1500, 2000, 1800, 2500, 3000] and revenue: [8000, 11000, 14500, 13000, 18000, 22000]. Run a Pearson correlation and linear regression on them."},{"title":"Detecting anomalies in sensor readings","prompt":"I have temperature readings from a sensor over 10 hours: [72, 71, 73, 70, 69, 95, 71, 72, 68, 70]. Can you compute the IQR and z-scores and tell me which readings are statistical outliers?"}],"resultDescription":"Returns a JSON object containing central tendency metrics (mean, median, mode), dispersion metrics (stddev, variance, IQR), quartile and percentile breakdowns, z-scores for each data point, IQR-based and z-score-based outlier flags per element, and optionally Pearson correlation coefficient and linear regression slope/intercept when a second series is provided.","failureModes":["Non-numeric values in the input array return a 400 validation error","Empty or single-element arrays may produce undefined results for stddev/variance","Mismatched lengths between the two series for correlation/regression return an error","Extremely large arrays may approach timeout limits","Missing required array parameter returns a 400 error"],"whenToPreferThis":"Choose this endpoint when you need a comprehensive, single-call statistical summary covering both descriptive stats and inferential metrics (correlation, regression) without setting up a local computation environment. Ideal for agents operating in lightweight or serverless contexts where importing a stats library is impractical. Prefer this over general-purpose code execution when you need a fast, deterministic, pay-per-call stats primitive with no setup overhead.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:58:36.459Z","isFirstParty":false}