Stats Compass MCP Server

<!-- mcp-name: io.github.oogunbiyi21/stats-compass --> <div align="center"> <img src="./assets/logo/logo1.png" alt="Stats Compass Logo" width="200"/>

stats-compass-mcp

Turn your LLM into a data analyst. Multiple data science tools via MCP.

</div> <img src="./assets/demos/stats_compass_mcp_1.gif" alt="Demo: Loading and exploring data" width="800"/>

Quick Start

pip install stats-compass-mcp

Claude Desktop

stats-compass-mcp install --client claude

VS Code (GitHub Copilot)

stats-compass-mcp install --client vscode

Claude Code (CLI)

claude mcp add stats-compass -- uvx stats-compass-mcp run

Note: The first connection may fail while uvx downloads the package. If this happens, disable and re-enable Stats Compass in your MCP settings — subsequent connections will be instant.

Restart your client and start asking questions about your data.

What Can It Do?

<img src="./assets/demos/stats_compass_mcp_2.gif" alt="Demo: Cleaning and transforming data" width="800"/>

Category	Examples
Data Loading	Load CSV/Excel, sample datasets, list DataFrames
Cleaning	Drop nulls, impute, dedupe, handle outliers
Transforms	Filter, groupby, pivot, encode, add columns
EDA	Describe, correlations, hypothesis tests, data quality
Visualization	Histograms, scatter, bar, ROC curves, confusion matrix
ML Workflows	Classification, regression, time series forecasting

Run stats-compass-mcp list-tools to see all available tools.

How to Prompt

Start your message with "Use stats compass to..." — this tells the AI to use the Stats Compass tools instead of trying to write code or use other methods.

Use stats compass to load ~/Downloads/sales.csv and run EDA on it
Use stats compass to find my CSV files in Downloads
Use stats compass to clean the dataset and handle missing values
Use stats compass to create a histogram of the price column
Use stats compass to test if there's a significant difference in scores between group A and B
Use stats compass to train a classification model to predict churn

Tip: Without this prefix, some AI clients may try to write Python code or use shell commands instead of the Stats Compass tools — especially for tasks like finding files on your machine.

Loading Files

Local mode: Start with "Use stats compass to load..." and provide the file path or folder.

Use stats compass to load the CSV at ~/Downloads/sales.csv
Use stats compass to find my data files in ~/Documents

Remote/HTTP mode: Use the upload feature (see below).

Remote Server Mode

For Docker deployments or multi-client setups:

stats-compass-mcp serve --port 8000

File Uploads

When running remotely, users can upload files via browser:

<img src="./assets/demos/upload_screenshot.png" alt="File Upload Interface" width="500"/>

You: I want to upload a file
AI: Open this link to upload: http://localhost:8000/upload?session_id=abc123

[Upload in browser]

You: I uploaded sales.csv
AI: ✅ Loaded sales.csv (1,000 rows × 8 columns)

Downloading Results

Export DataFrames, plots, and trained models:

You: Save the cleaned data as a CSV
AI: ✅ Saved. Download: http://localhost:8000/exports/.../cleaned_data.csv

Connect Clients to Remote Server

VS Code (native HTTP support):

{
  "servers": {
    "stats-compass": { "url": "http://localhost:8000/mcp" }
  }
}

Claude Desktop (via mcp-proxy):

{
  "mcpServers": {
    "stats-compass": {
      "command": "uvx",
      "args": ["mcp-proxy", "--transport", "streamablehttp", "http://localhost:8000/mcp"]
    }
  }
}

Docker

docker run -p 8000:8000 -e STATS_COMPASS_SERVER_URL=https://your-domain.com stats-compass-mcp

Client Compatibility

Client	Status
Claude Desktop	✅ Recommended
VS Code Copilot	✅ Supported
Claude Code CLI	✅ Supported
Cursor	✅ Supported
GPT / Gemini	⚠️ Partial

Configuration

Variable	Default	Description
STATS_COMPASS_PORT	8000	Server port
STATS_COMPASS_SERVER_URL	http://localhost:8000	Base URL for upload/download links
STATS_COMPASS_MAX_UPLOAD_MB	50	Max upload size

Development

See CONTRIBUTING.md for development setup.

🙏 Credits

Landing page template by ArtleSa (u/ArtleSa)

License

MIT