academic-stats-advisor · MCP server

An MCP (Model Context Protocol) server that lets an AI assistant — ChatGPT, Claude, Claude Code, Cursor, or any MCP client — directly call a statistician's decision logic instead of guessing. Point it at a study design and it returns the correct test, its assumptions, the SPSS menu path and R code, an APA reporting template, and an a-priori power analysis.

Built by Gan Lin. Dependency-light (only mcp), so it runs with a single command and deploys anywhere. (Repo academic-stats-mcp; server/package name academic-stats-advisor.)

⚠️ Decision-support for people who already know some statistics. It does not run your data or replace a statistician — always verify assumptions against your own dataset.

Tools

Covers the full classic tree: one-sample / independent / paired t-tests, Welch, Mann–Whitney, Wilcoxon, one-way / Welch / repeated-measures ANOVA, Kruskal–Wallis, Friedman, Pearson/Spearman, chi-square / Fisher / McNemar / goodness-of-fit, and Poisson/NB for counts.

Related MCP server: Stats Engine MCP Server

Run it (zero setup)

The server file carries its own dependencies (PEP 723), so uv needs nothing installed:

uv run server.py            # stdio  (for Claude Desktop / Claude Code / Cursor)
MCP_HTTP=1 uv run server.py # HTTP    (remote endpoint at http://localhost:8000/mcp)

Use it in Claude Code / Claude Desktop (local, stdio)

Add to your MCP config (Claude Desktop: claude_desktop_config.json; Claude Code: claude mcp add):

{
  "mcpServers": {
    "academic-stats-advisor": {
      "command": "uv",
      "args": ["run", "/absolute/path/to/academic-stats-mcp/server.py"]
    }
  }
}

Claude Code one-liner:

claude mcp add academic-stats-advisor -- uv run /absolute/path/to/academic-stats-mcp/server.py

Then just ask: "My outcome is a continuous score, two independent groups, the data are skewed — what test, and how do I report it?" — the model calls recommend_test and answers with the real decision logic.

Use it in ChatGPT / Claude.ai (remote, HTTP)

Deploy the HTTP transport, then add the resulting https://…/mcp URL as a custom connector.

• Render (free): this repo includes render.yaml + Dockerfile → New ▸ Blueprint → pick the repo. Endpoint: https://<service>.onrender.com/mcp.

• Docker anywhere: docker build -t stats-mcp . && docker run -p 8000:8000 stats-mcp → http://<host>:8000/mcp.

• Any Python host: python server.py --http, and set PUBLIC_HOST=<your-domain> so the Host check allows it (without it, DNS-rebinding protection is off so it still works behind any proxy).

License

MIT — see LICENSE.