📌Overview

Purpose: MCP Server aims to facilitate interactive data exploration, enabling users to derive actionable insights from complex datasets.

Overview: MCP Server is a user-friendly tool that acts as a personal Data Scientist assistant, simplifying the analysis of large data sets and allowing users to generate insights through a conversational interface. It supports various data exploration tasks and offers quick setups for users to begin their data analysis journey.

Key Features:

• Interactive Exploration: Allows users to input data topics and CSV file paths seamlessly, initiating a dialogue to explore different data aspects with ease.

• Versatile Tools: Includes functions like load-csv for loading datasets into a DataFrame and run-script for executing custom scripts, enhancing the server's functional capabilities for diverse analytical needs.

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MCP Server for Data Exploration

MCP Server is a versatile tool designed for interactive data exploration.
Your personal Data Scientist assistant, turning complex datasets into clear, actionable insights.

🚀 Try it Out

1. Download Claude Desktop
   Get it here

2. Install and Set Up
   On macOS, run the following command in your terminal:

   python setup.py
   

3. Load Templates and Tools
   Once the server is running, wait for the prompt template and tools to load in Claude Desktop.

4. Start Exploring
   Select the explore-data prompt template from MCP and begin your conversation by providing the required inputs:

   • csv_path: Local path to the CSV file
   • topic: The topic of exploration (e.g., "Weather patterns in New York" or "Housing prices in California")

Examples

These examples demonstrate how MCP Server can be used to explore data without human intervention.

Case 1: California Real Estate Listing Prices

• Kaggle Dataset: USA Real Estate Dataset
• Size: 2,226,382 entries (178.9 MB)
• Topic: Housing price trends in California
• Watch the video
• Data Exploration Summary

Case 2: Weather in London

• Kaggle Dataset: 2M+ Daily Weather History UK
• Size: 2,836,186 entries (169.3 MB)
• Topic: Weather in London
• Report: View Report
• Graphs:
  • London Temperature Trends
  • Temperature-Humidity Relationship by Season
  • Wind Direction Pattern by Season

📦 Components

Prompts

• explore-data: Tailored for data exploration tasks

Tools

1. load-csv
   Loads a CSV file into a DataFrame
   Arguments:

   • csv_path (string, required): Path to the CSV file
   • df_name (string, optional): Name for the DataFrame (defaults to df_1, df_2, etc.)

2. run-script
   Executes a Python script
   Arguments:

   • script (string, required): The script to execute

⚙️ Modifying the Server

Claude Desktop Configurations

• macOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%/Claude/claude_desktop_config.json

Development (Unpublished Servers)

"mcpServers": {
  "mcp-server-ds": {
    "command": "uv",
    "args": [
      "--directory",
      "/Users/username/src/mcp-server-ds",
      "run",
      "mcp-server-ds"
    ]
  }
}

Published Servers

"mcpServers": {
  "mcp-server-ds": {
    "command": "uvx",
    "args": [
      "mcp-server-ds"
    ]
  }
}

🛠️ Development

Building and Publishing

1. Sync Dependencies

   uv sync
   

2. Build Distributions

   uv build
   

   Generates source and wheel distributions in the dist/ directory.

3. Publish to PyPI

   uv publish
   

🤝 Contributing

Contributions are welcome! Whether you're fixing bugs, adding features, or improving documentation, your help makes this project better.

Reporting Issues

If you encounter bugs or have suggestions, open an issue. Include:

• Steps to reproduce (if applicable)
• Expected vs. actual behavior
• Screenshots or error logs (if relevant)

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

💬 Get in Touch

Questions or feedback? Open an issue or reach out to the maintainers.

About

This is an open source project run by ReadingPlus.AI LLC, open to contributions from the community.