Mcp Server For Data Exploration

What is MCP Server Data Exploration?

MCP Server Data Exploration is a public repository created by the user ### reading-plus-ai on GitHub. This project focuses on exploring data from the MCP server, providing users with tools and resources to analyze and visualize server data effectively. The repository serves as a platform for developers and data scientists to collaborate, share insights, and enhance their understanding of server data dynamics.

Features of MCP Server Data Exploration

• Data Visualization: The repository includes various tools for visualizing server data, making it easier to interpret complex datasets.
• Collaboration: It encourages contributions from the community, allowing users to fork the repository and add their enhancements or modifications.
• Open Source: Being a public repository, it is accessible to anyone interested in exploring server data, fostering an open-source environment.
• Documentation: Comprehensive documentation is provided to guide users through the setup and usage of the tools available in the repository.
• Active Community: With a growing number of stars and forks, the repository has an active community that contributes to its development and improvement.

How to Use MCP Server Data Exploration

1. Access the Repository: Visit the MCP Server Data Exploration GitHub page.
2. Clone the Repository: Use Git to clone the repository to your local machine:

   git clone https://github.com/reading-plus-ai/mcp-server-data-exploration.git
   

3. Explore the Documentation: Review the README file and other documentation provided within the repository to understand how to use the tools and features.
4. Run the Tools: Follow the instructions to set up and run the data exploration tools on your local machine.
5. Contribute: If you have improvements or new features to add, feel free to fork the repository, make your changes, and submit a pull request.

Frequently Asked Questions

What programming languages are used in this repository?

The MCP Server Data Exploration repository primarily uses Python for data analysis and visualization, along with other languages as needed for specific tools.

Can I contribute to the MCP Server Data Exploration project?

Yes! Contributions are welcome. You can fork the repository, make your changes, and submit a pull request for review.

Is there a license for this repository?

Yes, the MCP Server Data Exploration repository is licensed under the MIT License, allowing for free use, modification, and distribution.

How can I stay updated on changes to the repository?

You can watch the repository on GitHub to receive notifications about updates, issues, and discussions related to the project.

Where can I find help if I encounter issues?

You can open an issue on the GitHub repository page to report problems or ask for help from the community. Additionally, check the documentation for troubleshooting tips.

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.

<a href="https://glama.ai/mcp/servers/hwm8j9c422"><img width="380" height="200" src="https://glama.ai/mcp/servers/hwm8j9c422/badge" alt="mcp-server-data-exploration MCP server" /></a>

🚀 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
   • 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 are examples of how you can use MCP Server to explore data without any 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

• 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

    <img width="1622" alt="Screenshot 2024-12-09 at 12 48 56 AM" src="https://github.com/user-attachments/assets/9e70fe97-8af7-4221-b1e7-00197c88bb47">

  • Temperature-Humidity Relationship by Season

    <img width="1623" alt="Screenshot 2024-12-09 at 12 47 54 AM" src="https://github.com/user-attachments/assets/f4ac60a8-30e3-4b10-b296-ba412c2922fa">

  • Wind Direction Pattern by Season

    <img width="1622" alt="Screenshot 2024-12-09 at 12 47 00 AM" src="https://github.com/user-attachments/assets/2db01054-f948-4d2e-ba39-8de8fa59f83d">

📦 Components

Prompts

• explore-data: Tailored for data exploration tasks

Tools

1. load-csv

   • Function: 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., if not provided

2. run-script

   • Function: 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 in the issues section. 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? Feedback? Open an issue or reach out to the maintainers. Let's make this project awesome together!

About

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