stats-compass-mcp 0.1.0

MCP server exposing stats-compass-core tools to LLMs like ChatGPT, Claude, and Gemini

stats-compass-mcp

MCP server that exposes stats-compass-core tools to LLMs like ChatGPT, Claude, and Gemini.

What is this?

This package turns the stats-compass-core toolkit into an MCP (Model Context Protocol) server. Once running, any MCP-compatible client (ChatGPT, Claude, Cursor, VS Code, etc.) can use your data analysis tools directly.

Installation

pip install stats-compass-mcp

Quick Start

Start the server

stats-compass-mcp serve

Configure your MCP client

For Claude Desktop (Recommended)

The easiest way to run this server is using uvx (part of the uv toolkit), which downloads and runs the server in an isolated environment without installation.

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "stats-compass": {
      "command": "uvx",
      "args": ["stats-compass-mcp", "serve"]
    }
  }
}

Manual Installation

If you prefer to install it globally:

pip install stats-compass-mcp

Then configure your client:

{
  "mcpServers": {
    "stats-compass": {
      "command": "stats-compass-mcp",
      "args": ["serve"]
    }
  }
}

Available Tools

Once connected, the following tools are available to LLMs:

Data Loading & Management

• load_csv - Load CSV files into state
• load_dataset - Load built-in sample datasets
• list_dataframes - List all DataFrames in state
• get_schema - Get column types and info
• get_sample - Preview rows from a DataFrame

Data Cleaning

• dropna - Remove missing values
• apply_imputation - Fill missing values
• dedupe - Remove duplicate rows
• handle_outliers - Detect and handle outliers

Transforms

• filter_dataframe - Filter rows by condition
• groupby_aggregate - Group and aggregate data
• pivot - Pivot tables
• add_column - Add calculated columns
• rename_columns - Rename columns
• drop_columns - Remove columns

EDA & Statistics

• describe - Summary statistics
• correlations - Correlation matrix
• hypothesis_tests - T-tests, chi-square, etc.
• data_quality - Data quality report

Visualization

• histogram - Distribution plots
• scatter_plot - Scatter plots
• bar_chart - Bar charts
• lineplot - Line plots

Machine Learning

• train_linear_regression - Linear regression
• train_logistic_regression - Logistic regression
• train_random_forest_classifier - Random forest classification
• train_random_forest_regressor - Random forest regression
• evaluate_model - Model evaluation metrics

Time Series (ARIMA)

• check_stationarity - ADF/KPSS tests
• fit_arima - Fit ARIMA models
• forecast_arima - Generate forecasts
• find_optimal_arima - Auto parameter search

How It Works

┌─────────────────────────────────────────────────────────────┐
│                    MCP Client                               │
│         (ChatGPT, Claude, Cursor, VS Code)                  │
└─────────────────────────┬───────────────────────────────────┘
                          │ MCP Protocol
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                stats-compass-mcp                            │
│  ┌─────────────────────────────────────────────────────┐    │
│  │              MCP Server (this package)              │    │
│  │  • Registers tools from stats-compass-core          │    │
│  │  • Manages DataFrameState per session               │    │
│  │  • Converts tool results to MCP responses           │    │
│  └─────────────────────────────────────────────────────┘    │
│                          │                                  │
│                          ▼                                  │
│  ┌─────────────────────────────────────────────────────┐    │
│  │           stats-compass-core (PyPI)                 │    │
│  │  • DataFrameState (server-side state)               │    │
│  │  • 40+ deterministic tools                          │    │
│  │  • Pydantic schemas for all inputs/outputs          │    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘

Development

# Clone and install
git clone https://github.com/oogunbiyi21/stats-compass-mcp.git
cd stats-compass-mcp
poetry install

# Run tests
poetry run pytest

# Run the server locally
poetry run stats-compass-mcp serve

Related Projects

• stats-compass-core - The underlying toolkit
• stats-compass - Streamlit chat UI for data analysis

License

MIT