MCP Server Data Exploration

Community Data and databases

Autonomous data exploration of CSV datasets with generated insights.

What can you do with MCP Server Data Exploration?

  • load-csv
  • run-script

Install

The installation steps are in the documentation below. Documentation

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. MCP Server for Data Exploration
  2. 🚀 Try it Out
  3. Examples
  4. 📦 Components
  5. ⚙️ Modifying the Server
  6. 🛠️ Development
  7. 🤝 Contributing
  8. 📜 License
  9. 💬 Get in Touch
  10. About

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

Case 2: Weather in London

📦 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Development (Unpublished Servers)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.

Frequently asked questions

What is MCP Server Data Exploration?

Autonomous data exploration of CSV datasets with generated insights.

How do I install MCP Server Data Exploration?

Follow the installation steps in its documentation, shown on this page and in its repository.

Is MCP Server Data Exploration open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains MCP Server Data Exploration?

It is published by reading-plus-ai. AI.tn lists it but does not maintain or endorse it.

From the community awesome-mcp-servers lists and the project's GitHub repository. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.