stats-compass-mcp 0.1.11

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

stats-compass-mcp

A stateful, MCP-compatible toolkit of pandas-based data tools for AI-powered data analysis.

⚠️ Status: Early developer release (v0.1)
Optimized for Claude models
Gemini and GPT tool calling may be inconsistent.

stats-compass-mcp

Stats Compass MCP turns the stats-compass-core toolkit into an MCP server that AI agents can call in a reproducible, stateful way across workflows.

What is this?

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

Client Compatibility

Client	Status	Notes
Claude Desktop	✅ Supported	Recommended. Best tool selection.
VS Code Copilot Chat	✅ Supported (Beta)	Native MCP integration. May need restart after config changes.
Cursor	⚠️ Experimental	Pending official MCP release.
GPT / ChatGPT	⚠️ Partial	Tool calling may be inconsistent with large toolsets.
Gemini	⚠️ Unstable	May throw errors with complex schemas.
Roo Code	❌ Unsupported	Incompatible JSON Schema validation.

Installation

pip install stats-compass-mcp

Prerequisite: The MCP configurations below use uvx, which requires uv to be installed.

⚠️ Important Note on Data Loading

Drag-and-drop file uploads are NOT supported. To load data, you must provide the absolute file path to the file on your local machine.

• ✅ "Load the file at /Users/me/data.csv"
• ❌ Dragging data.csv into the chat window

Quick Start

Start the server

stats-compass-mcp serve

Configure your MCP client

1. Claude Desktop (Recommended)

You can configure Claude Desktop automatically:

# Install the package
pip install stats-compass-mcp

# Run the auto-configuration
stats-compass-mcp install

Or manually add this to your claude_desktop_config.json:

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

2. VS Code (GitHub Copilot)

VS Code has native MCP support via GitHub Copilot:

# Install the package
pip install stats-compass-mcp

# Run the auto-configuration
stats-compass-mcp install-vscode

Or manually add this to your VS Code mcp.json (located at ~/Library/Application Support/Code/User/mcp.json on macOS):

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

3. Claude Code (CLI)

To use Stats Compass with the Claude CLI:

claude mcp add stats-compass -- uvx stats-compass-mcp 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
• confusion_matrix_plot - Confusion matrix heatmap
• roc_curve_plot - ROC curves for classification
• precision_recall_curve_plot - Precision-recall curves
• feature_importance - Feature importance bar charts

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
• train_gradient_boosting_classifier - Gradient boosting classification
• train_gradient_boosting_regressor - Gradient boosting regression
• evaluate_classification_model - Classification metrics (accuracy, precision, recall, F1)
• evaluate_regression_model - Regression metrics (R², MAE, RMSE)

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)               │    │
│  │  • 20+ deterministic tools                          │    │
│  │  • Pydantic schemas for all inputs/outputs          │    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘

Contributing

Architecture Overview

If you want to contribute to stats-compass-mcp, it helps to understand how the pieces fit together:

1. Entry Point: The pyproject.toml defines the script stats-compass-mcp = "stats_compass_mcp.cli:main". This is what runs when you execute the command.
2. CLI (cli.py): Parses command-line arguments and launches the server.
3. Server (server.py):
   • Initializes a DataFrameState (from stats-compass-core) to hold data in memory during the session.
   • Discovers tools dynamically using registry.auto_discover() and get_all_tools().
   • Registers list_tools and call_tool handlers to communicate with the MCP client.
   • Executes tools by injecting the session state into the function calls.
4. Communication: Uses stdio transport to exchange JSON-RPC messages with the client (Claude, etc.).

Local Development

1. Clone and Install:

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

2. Configure for Development: You can automatically configure your MCP clients to use your local development version (instead of the published PyPI version):

   # For Claude Desktop
   poetry run stats-compass-mcp install --dev
   
   # For VS Code (GitHub Copilot)
   poetry run stats-compass-mcp install-vscode --dev
   

3. Run the Server:

   poetry run stats-compass-mcp serve
   

4. Test with MCP Inspector: You can use the MCP Inspector to test the server interactively:

   npx @modelcontextprotocol/inspector poetry run stats-compass-mcp serve
   

Known Limitations

• Local files only: The MCP server runs on your machine. It cannot access files in cloud sandboxes or drag-and-drop uploads. You must provide absolute file paths.
• One MCP client at a time: Running multiple clients connected to the same server may cause state conflicts.
• VS Code schema caching: VS Code caches tool schemas aggressively. After updating the package, restart VS Code or run stats-compass-mcp install-vscode again.
• Gemini instability: Gemini clients may fail with 400 errors on complex tool schemas. This is a known Gemini limitation, not a Stats Compass bug.

Troubleshooting

Issue	Solution
Tools don't appear in VS Code	Restart VS Code. Check mcp.json path is correct.
"File not found" errors	Use absolute paths, not relative. Check file exists with list_files.
Schema validation errors	Ensure you're on the latest version. Run pip install --upgrade stats-compass-mcp.
Gemini 400 errors	Known issue. Use Claude Desktop or VS Code Copilot instead.
Stale tools after update	Run stats-compass-mcp install-vscode --dev to refresh config. Restart VS Code.

Related Projects

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

License

MIT