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24K Labs Stats Toolkit
x402
24K Labs Stats Toolkit is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-13).
Computes descriptive statistics (mean/median/mode, stddev/variance, quartiles/percentiles, z-scores, IQR), flags outliers, and optionally computes Pearson correlation and linear regression against a second data series.
Price / call$0.003
Success—
StatusUnknown
How this service works
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.
Output
Returns a JSON object containing descriptive statistics (mean, median, mode, standard deviation, variance), quartiles, requested percentile values, z-scores for each data point, IQR value, outlier flags (IQR-based and/or z-score-based), and optionally Pearson correlation coefficient and linear regression slope/intercept against a second series.
Example use cases
Outlier detection in sensor data
“I have a list of temperature readings from my IoT sensors — [22.1, 21.8, 22.5, 22.0, 75.3, 21.9, 22.2, 22.4] — can you calculate the full stats summary and flag any outliers using both z-score and IQR methods?”
Correlating marketing spend and revenue
“I want to know if my weekly ad spend correlates with weekly revenue — ad spend is [500, 800, 600, 1200, 900, 1100] and revenue is [4200, 6100, 4800, 9500, 7100, 8800]. Can you compute the Pearson correlation and fit a linear regression so I can see the relationship?”
Summarizing exam score distribution
“Here are exam scores for my class: [45, 67, 72, 55, 88, 91, 60, 74, 82, 49, 95, 58]. Can you give me the mean, median, mode, standard deviation, quartiles, the 25th and 75th percentiles, and tell me if any scores are statistical outliers?”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/stats-toolkitWhen to prefer
Choose this endpoint when you need a comprehensive, single-call statistical summary of a numeric dataset rather than computing individual statistics separately. It is ideal for data quality checks, exploratory data analysis, outlier detection pipelines, or whenever you need correlation and regression alongside descriptive stats without stitching together multiple tools. Prefer it over general-purpose LLM math when you need precise, reproducible numerical results.
Side effects
- Empty or non-numeric array returns validation error
- Single-element array may produce undefined stddev or mode
- Non-numeric values in array cause parse/type error
- Second series of mismatched length causes regression/correlation error
- Extremely large arrays may hit payload size limits
- Missing required data field returns 400 bad request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| data | array |
Output
Schema · Response
{
"type": "json",
"example": {
"mean": 5,
"stdev": 2,
"median": 4.5,
"outliers": []
}
}Tags
x402
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