MCP Server Data Exploration

Communauté Données et bases de données

Autonomous data exploration of CSV datasets with generated insights.

Que pouvez-vous faire avec MCP Server Data Exploration ?

  • load-csv
  • run-script

Installation

Les étapes d'installation sont dans la documentation ci-dessous. Documentation

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Sommaire
  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Development (Unpublished Servers)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.

Questions fréquentes

Qu'est-ce que MCP Server Data Exploration ?

Autonomous data exploration of CSV datasets with generated insights.

Comment installer MCP Server Data Exploration ?

Suivez les étapes d'installation de sa documentation, affichée sur cette page et dans son dépôt.

MCP Server Data Exploration est-il open source ?

Oui. Son code est sur GitHub sous licence MIT.

Qui maintient MCP Server Data Exploration ?

Il est publié par reading-plus-ai. AI.tn le référence sans le maintenir ni le recommander.

Issu des listes communautaires awesome-mcp-servers et du dépôt GitHub du projet. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.