databutler-stats
Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (1)
- LOWTool 'confidence_interval' description lacks action verbin confidence_interval
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"databutler-stats": {
"url": "https://databutler.dev/api/mcp/stats"
}
}
}Remote-Endpunkte
https://databutler.dev/api/mcp/statsstreamable-httpWas es kann
Tool-Inventar
Tools (6)
⚪descriptive_stats(data)
Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.
Eingabe-Schema
{
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "number"
}
}
},
"required": [
"data"
]
}⚪distribution(dist, params, at, p)
Evaluate a probability distribution (normal, t, chi2, binomial, poisson): pdf/pmf and cdf at a value, and/or the quantile at a probability, plus mean & variance. Params per dist: normal {mean,sd}, t {df}, chi2 {df}, binomial {n,p}, poisson {lambda}.
Eingabe-Schema
{
"type": "object",
"properties": {
"dist": {
"type": "string",
"enum": [
"normal",
"t",
"chi2",
"binomial",
"poisson"
]
},
"params": {
"type": "object"
},
"at": {
"type": "number",
"description": "value to evaluate pdf/pmf and cdf at"
},
"p": {
"type": "number",
"description": "probability to get the quantile for (0-1)"
}
},
"required": [
"dist"
]
}🟢hypothesis_test(test, data, data1, data2, mu0, ...)
Run a significance test and get the statistic, p-value, and a plain-language interpretation with assumptions. test = one-sample-t {data, mu0}, two-sample-t {data1, data2}, one-proportion-z {successes, n, p0}, two-proportion-z {successes1,n1,successes2,n2}, chi2-gof {observed, expected?}, chi2-independence {table}. Optional tail: two-sided (default) | greater | less; alpha default 0.05.
Eingabe-Schema
{
"type": "object",
"properties": {
"test": {
"type": "string"
},
"data": {
"type": "array",
"items": {
"type": "number"
}
},
"data1": {
"type": "array",
"items": {
"type": "number"
}
},
"data2": {
"type": "array",
"items": {
"type": "number"
}
},
"mu0": {
"type": "number"
},
"successes": {
"type": "number"
},
"n": {
"type": "number"
},
"p0": {
"type": "number"
},
"successes1": {
"type": "number"
},
"n1": {
"type": "number"
},
"successes2": {
"type": "number"
},
"n2": {
"type": "number"
},
"observed": {
"type": "array",
"items": {
"type": "number"
}
},
"expected": {
"type": "array",
"items": {
"type": "number"
}
},
"table": {
"type": "array"
},
"tail": {
"type": "string"
},
"alpha": {
"type": "number"
}
},
"required": [
"test"
]
}⚪confidence_interval(kind, confidence, data, n, mean, ...)
Confidence interval for a mean (t-based; from data, or n/mean/sd) or a proportion (Wilson; successes/n). kind = mean | proportion; confidence default 0.95.
Eingabe-Schema
{
"type": "object",
"properties": {
"kind": {
"type": "string",
"enum": [
"mean",
"proportion"
]
},
"confidence": {
"type": "number"
},
"data": {
"type": "array",
"items": {
"type": "number"
}
},
"n": {
"type": "number"
},
"mean": {
"type": "number"
},
"sd": {
"type": "number"
},
"successes": {
"type": "number"
}
}
}🟡bayes_update(hypotheses)
Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors. Priors are renormalised to sum to 1.
Eingabe-Schema
{
"type": "object",
"properties": {
"hypotheses": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"prior": {
"type": "number"
},
"likelihood": {
"type": "number"
}
},
"required": [
"prior",
"likelihood"
]
}
}
},
"required": [
"hypotheses"
]
}⚪linear_regression(x, y)
Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.
Eingabe-Schema
{
"type": "object",
"properties": {
"x": {
"type": "array",
"items": {
"type": "number"
}
},
"y": {
"type": "array",
"items": {
"type": "number"
}
}
},
"required": [
"x",
"y"
]
}Community
Nachweis