INDIVIDUAL MCP TOOL

bayes_update

Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors.

databutler.devnone authenticationAvailability not checked

LIVE ENDPOINT

https://databutler.dev/api/mcp/stats

No auth detected

Connect to this endpoint to inspect the live schema for bayes_update and invoke it with your own arguments.

{}

Potential side effects detected · medium confidence · heuristic, not a guarantee.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

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

Claude Code

.mcp.json

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

Claude Desktop

Settings → Connectors → Add custom connector

Name: databutler-stats
Remote MCP URL: https://databutler.dev/api/mcp/stats

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

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

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

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

Generic MCP

Client-specific MCP configuration

{
  "name": "databutler-stats",
  "transport": "streamable-http",
  "url": "https://databutler.dev/api/mcp/stats"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

descriptive_stats — Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.
distribution — 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.
hypothesis_test — Run a significance test and get the statistic, p-value, and a plain-language interpretation with assumptions.
confidence_interval — Confidence interval for a mean (t-based; from data, or n/mean/sd) or a proportion (Wilson; successes/n).
linear_regression — Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.