MCP server

Databutler Stats

io.github.physics-star-cat/databutler-stats

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

6

Token cost

About 922 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http databutler-stats https://databutler.dev/api/mcp/stats

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.databutler-stats]
url = "https://databutler.dev/api/mcp/stats"

Gemini CLI

{"mcpServers":{"databutler-stats":{"httpUrl":"https://databutler.dev/api/mcp/stats"}}}

Cursor

{"mcpServers":{"databutler-stats":{"url":"https://databutler.dev/api/mcp/stats"}}}

VS Code

{"servers":{"databutler-stats":{"type":"http","url":"https://databutler.dev/api/mcp/stats"}}}

Antigravity

agy mcp add databutler-stats https://databutler.dev/api/mcp/stats

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"databutler-stats","server_url":"https://databutler.dev/api/mcp/stats","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"databutler-stats","server_url":"https://databutler.dev/api/mcp/stats"}

Tools

• bayes_update ~138 tokens
  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.
• confidence_interval ~139 tokens
  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.
• descriptive_stats ~76 tokens
  Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.
• distribution ~176 tokens
  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}.
• hypothesis_test ~305 tokens
  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-indepe
• linear_regression ~88 tokens
  Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.

Browse

• GitHub tools
• PDF tools
• Project management
• GitHub and code hosting
• Government and public data

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