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Sindri Descriptive Statistics Summary
x402
Sindri Descriptive Statistics Summary is a paid API for AI agents from x402.outpimp.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Computes descriptive statistics (mean, median, variance, standard deviation, and 25th/75th/90th percentiles) over a numeric array of up to 10,000 elements using R/numpy-compatible interpolation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Descriptive statistics summary (mean, median, variance, standard deviation, and 25th/75th/90th percentiles via linear interpolation, matching R and numpy's default "type=7" method) over a numeric array of up to 10,000 elements.
Output
Returns a JSON object containing the mean, median, variance, standard deviation, and the 25th, 75th, and 90th percentiles computed via linear interpolation (matching R and numpy's type=7 default method) for the provided numeric array.
Example use cases
Analyze survey response scores
“I have a list of 200 survey satisfaction scores and I need the mean, median, standard deviation, and 25th/75th/90th percentiles so I can understand the distribution — here are all the values: [82, 91, 74, ...]”
QA check on sensor readings
“Here are temperature readings from our sensor array over the last hour — can you compute descriptive stats including variance and percentiles so I can flag any anomalies? Values: [98.2, 97.8, 99.1, 101.3, 97.5, ...]”
Finance team sales distribution summary
“Can you summarize these monthly sales figures with mean, median, variance, standard deviation, and the 25th, 75th, and 90th percentiles? The numbers are: [15200, 18700, 14300, 22000, 19500, 16800, 21000, 17400]”
Technical details
Endpoint
POSThttps://x402.outpimp.com/statisticsSummaryWhen to prefer
Choose this endpoint when you need R/numpy-compatible descriptive statistics (including percentiles via linear interpolation) computed in a single API call over a numeric array. It is ideal for agents that need reproducible, standard statistical summaries without spinning up a Python or R runtime. Prefer this over manual computation when working with arrays up to 10,000 elements and needing consistent cross-platform statistical outputs.
Side effects
- Empty array input returns an error since at least one value is required
- Array exceeding 10,000 elements is rejected with a size limit error
- Non-numeric values in the array cause a validation error
- Malformed JSON body returns a 400 bad request
- Payment not included or insufficient USDC triggers a 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| values | array | Non-empty array of numbers, up to 10,000 elements. |
Tags
x402
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