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智能数据探索

启用对基于.csv的数据集进行自主数据探索,以最少的努力提供智能洞察。

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README

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.

mcp-server-data-exploration MCP server

🚀 Try it Out

  1. Download Claude Desktop

  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

Watch the video

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Development (Unpublished Servers)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.

help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端