Mcp Server Data Exploration

What is Mcp Server Data Exploration about?

Mcp Server Data Exploration provides an interactive environment where users can feed CSV datasets to a Claude‑based data‑science assistant. The server supplies prompts (e.g., explore-data) and tools (load-csv, run-script) that let the assistant automatically analyse, visualise, and summarise the data without manual coding.

How to use Mcp Server Data Exploration?

Download Claude Desktop – obtain it from the official link.
Install the server – on macOS run python setup.py (or the equivalent command for your OS).
Start the server – launch Claude Desktop, wait for the prompt template and tools to load.
Run a data‑exploration session – select the explore-data prompt, then provide:
- csv_path: local path to the CSV file.
- topic: a natural‑language description of the analysis you want (e.g., "Housing price trends in California").
The assistant will load the CSV, run optional Python scripts, and return insights, charts, and a written report.

Key features of Mcp Server Data Exploration

Prompt‑driven exploration – pre‑built explore-data prompt tailored for analytics.
Built‑in tools:
- load-csv to import files directly into a DataFrame.
- run-script to execute arbitrary Python code for custom processing.
Easy deployment – simple python setup.py installation; compatible with both unpublished and published MCP server configurations.
Rich output – generates textual summaries, visualisations, and downloadable artifacts.
Open‑source MIT‑licensed – community contributions welcomed.

Use cases of Mcp Server Data Exploration

Real‑estate market analysis – ingest large listings datasets to uncover price trends, regional differences, and forecasting opportunities.
Weather pattern investigation – explore multi‑million‑row climate records to produce temperature, humidity, and wind‑direction insights.
Rapid prototyping for data‑science projects – let the AI assistant perform exploratory data analysis (EDA) before writing production code.
Educational demos – showcase end‑to‑end data analysis workflows without requiring students to write code.

FAQ from the Mcp Server Data Exploration

Q: Which operating systems are supported? A: The setup instructions target macOS, but the Python‑based server can run on any OS with Python installed.

Q: Do I need an API key? A: The server relies on Claude Desktop for model access, so you need a valid Claude account but no additional API key in the server config.

Q: How large a CSV can be processed? A: Examples show files up to ~3 M rows (≈170 MB) processed successfully; larger files depend on available memory.

Q: Can I add custom prompts or tools? A: Yes – MCP servers are extensible; you can define new prompts or Python scripts and expose them via the run-script tool.

Q: How do I publish the server for others to use? A: Follow the "Published Servers" JSON snippet in the README, adjusting the command to uvx or the appropriate package manager for your environment.

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

Download Claude Desktop
- Get it here
Install and Set Up
- On macOS, run the following command in your terminal:
```
python setup.py
```
Load Templates and Tools
- Once the server is running, wait for the prompt template and tools to load in Claude Desktop.
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:

📦 Components

Prompts

explore-data: Tailored for data exploration tasks

Tools

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
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

Sync Dependencies
```
uv sync
```
Build Distributions
```
uv build
```
Generates source and wheel distributions in the dist/ directory.
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.