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Stats Compass

Analyze local or uploaded datasets with pandas-based tools for loading, cleaning, visualization, statistics, and machine learning.

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Install Stats Compass

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "stats-compass": {
      "command": "uvx",
      "args": [
        "stats-compass-mcp"
      ],
      "env": {
        "STATS_COMPASS_PORT": "YOUR_VALUE_HERE",
        "STATS_COMPASS_SERVER_URL": "YOUR_VALUE_HERE",
        "STATS_COMPASS_MAX_UPLOAD_MB": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx stats-compass-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 19d ago.

Overview

Stats Compass MCP server exposes more than 50 pandas-powered tools for working with data through an MCP-compatible AI client. It supports loading CSV and Excel files, cleaning and transforming DataFrames, exploratory analysis, visualizations, statistical tests, and selected machine learning workflows. Local mode reads files from paths or folders, while remote mode provides browser uploads and downloadable results. Reach for it when you want an AI client to operate on datasets through data-science tools rather than generate analysis code.

Use cases

  • Load a CSV or Excel file and inspect its structure
  • Clean missing values, duplicates, and outliers
  • Create charts from selected DataFrame columns
  • Run hypothesis tests between groups
  • Train a classification or regression model

Key features

  • CSV and Excel loading
  • Data cleaning and transformation
  • Exploratory data analysis and statistical tests
  • Charts and model evaluation plots
  • Classification, regression, and time-series workflows
  • Remote uploads and downloadable results

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

6/6 checks healthy over the last 45d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 5d ago via OSV.dev · stats-compass-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars17Stargazers on the source repository.
Last commit3d agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity5 viewsConfig copies, upvotes, and views on AllMCPs.

How Stats Compass works

What Stats Compass MCP server does

Stats Compass MCP server gives an MCP-compatible AI client a collection of more than 50 tools for common data-science tasks. It belongs to the 🧮 Data Science Tools category and is intended for workflows that move from loading data through analysis and modeling.

The available areas include CSV and Excel loading, sample datasets, and DataFrame listing. Cleaning operations cover null handling, imputation, duplicate removal, and outlier handling. Transformation tools support filtering, grouping, pivoting, encoding, and adding columns. For analysis, the server provides descriptive statistics, correlation checks, hypothesis tests, and data-quality work. Visualization options include histograms, scatter plots, bar charts, ROC curves, and confusion matrices. Machine learning workflows include classification, regression, and time-series forecasting.

How it works

In local mode, an AI client can ask Stats Compass to load a file by path or locate data files in a folder. The resulting data can then be passed through cleaning, transformation, exploratory analysis, visualization, or modeling operations. Prompts beginning with “Use stats compass to...” are recommended because some clients may otherwise write Python or use shell commands instead of selecting the server’s tools.

Stats Compass MCP server can also run as a remote HTTP service. The service exposes an MCP endpoint and provides a browser upload flow for files. Results such as exported DataFrames, plots, and trained models can be downloaded through generated links. This mode is suited to Docker deployments or setups where multiple clients connect to one running service.

Setup and configuration

Install the Python package with pip install stats-compass-mcp. The project requires Python 3.11 or newer. Its installer can configure Claude Desktop with stats-compass-mcp install --client claude or VS Code with stats-compass-mcp install --client vscode. Claude Code can add the server with claude mcp add stats-compass -- uvx stats-compass-mcp run.

For remote use, start the service with stats-compass-mcp serve --port 8000. VS Code can connect to http://localhost:8000/mcp; Claude Desktop can use mcp-proxy to bridge to the streamable HTTP endpoint. Docker is also supported, including a published port and the STATS_COMPASS_SERVER_URL setting.

The configurable environment variables are STATS_COMPASS_PORT, which defaults to 8000, STATS_COMPASS_SERVER_URL, which defaults to http://localhost:8000, and STATS_COMPASS_MAX_UPLOAD_MB, which defaults to 50.

Limitations and notes

Stats Compass MCP server’s documented client support includes Claude Desktop, VS Code Copilot, Claude Code CLI, and Cursor. GPT and Gemini support is marked partial. Remote operation requires the upload workflow for bringing files into the service, while local operation expects paths or folders accessible to the running setup.

The first Claude Code connection may fail while uvx downloads the package. Disabling and re-enabling the server in the MCP settings is documented as a recovery step. The server is released under the MIT license.

Read the full README View source on GitHub

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Frequently Asked Questions about Stats Compass

Stats Compass is an MCP server that connects AI clients to local files or remotely uploaded datasets and provides tools for loading, cleaning, transforming, exploring, visualizing, and modeling data. Its documented workflows include CSV and Excel input, statistical tests, charts, classification, regression, and time-series forecasting.

Install it with `pip install stats-compass-mcp`. The package includes setup commands for Claude Desktop and VS Code, while Claude Code can add it with `claude mcp add stats-compass -- uvx stats-compass-mcp run`. Python 3.11 or newer is required.

Yes, Cursor is listed as supported. Connect Cursor to the configured Stats Compass MCP server and use prompts that begin with “Use stats compass to...” to direct requests to its data-science tools.

Run `stats-compass-mcp serve --port 8000`. Clients can connect to the MCP endpoint at `http://localhost:8000/mcp`; remote users can upload files through the browser upload flow and download exported DataFrames, plots, or trained models.

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