databeak 0.0.2

DataBeak: MCP server for comprehensive CSV file operations with pandas-based tools

DataBeak

AI-Powered CSV Processing via Model Context Protocol

Transform how AI assistants work with CSV data. DataBeak provides 40+ specialized tools for data manipulation, analysis, and validation through the Model Context Protocol (MCP).

Features

• 🔄 Complete Data Operations - Load, transform, analyze, and export CSV data
• 📊 Advanced Analytics - Statistics, correlations, outlier detection, data profiling
• ✅ Data Validation - Schema validation, quality scoring, anomaly detection
• 💾 Auto-Save & History - Never lose work with configurable strategies and undo/redo
• ⚡ High Performance - Handles large datasets with streaming and chunking
• 🔒 Session Management - Multi-user support with isolated sessions
• 🌟 Production Quality - Zero ruff violations, 100% mypy compliance, comprehensive test coverage

Getting Started

The fastest way to use DataBeak is with uvx (no installation required):

For Claude Desktop

Add this to your MCP Settings file:

{
  "mcpServers": {
    "databeak": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/jonpspri/databeak.git",
        "databeak"
      ]
    }
  }
}

For Other AI Clients

DataBeak works with Continue, Cline, Windsurf, and Zed. See the installation guide for specific configuration examples.

Quick Test

Once configured, ask your AI assistant:

"Load a CSV file and show me basic statistics"
"Remove duplicate rows and export as Excel"
"Find outliers in the price column"

Documentation

📚 Complete Documentation

• Installation Guide - Setup for all AI clients
• Quick Start Tutorial - Learn in 10 minutes
• API Reference - All 40+ tools documented
• Architecture - Technical details

Environment Variables

Variable	Default	Description
DATABEAK_MAX_FILE_SIZE_MB	1024	Maximum file size
DATABEAK_CSV_HISTORY_DIR	"."	History storage location
DATABEAK_SESSION_TIMEOUT	3600	Session timeout (seconds)

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run quality checks: uv run -m pytest
5. Submit a pull request

Note: All changes must go through pull requests. Direct commits to main are blocked by pre-commit hooks.

Development

# Setup development environment
git clone https://github.com/jonpspri/databeak.git
cd databeak
uv sync

# Run the server locally
uv run databeak

# Run tests
uv run -m pytest tests/unit/          # Unit tests (primary)
uv run -m pytest                      # All tests

# Run quality checks
uv run ruff check
uv run mypy

Testing Structure

DataBeak currently focuses on comprehensive unit testing with future plans for integration and E2E testing:

• Unit Tests (tests/unit/) - Fast, isolated module tests (current focus)
• Integration Tests (tests/integration/) - Future: FastMCP Client-based testing
• E2E Tests (tests/e2e/) - Future: Complete workflow validation

Current Test Execution:

uv run pytest -n auto tests/unit/          # Run unit tests (primary)
uv run pytest -n auto --cov=src/databeak   # Run with coverage analysis

See Testing Guide for comprehensive testing details.

License

Apache 2.0 - see LICENSE file.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: jonpspri.github.io/databeak