Data Exploration Assistant MCP Server

Data Science ToolsPython

Interactive data exploration and visualization tool for large datasets

Available Tools

load-csv

Loads a CSV file for data exploration and analysis

analyze-data

Performs statistical analysis on the loaded dataset

create-visualization

Generates visualizations based on the dataset and analysis

generate-report

Creates a comprehensive report with insights from the data analysis

Data Exploration Assistant is a powerful MCP server designed to help users analyze and visualize large datasets without writing code. It provides automated data profiling, statistical analysis, and visualization capabilities that transform complex CSV data into clear, actionable insights. The server handles multi-million row datasets efficiently, automatically detecting patterns, outliers, and relationships between variables. With its intuitive interface through Claude Desktop, users can explore data through natural language queries and receive comprehensive reports with visualizations tailored to their specific topics of interest.

Getting Started with Data Exploration Assistant

Prerequisites

Claude Desktop application (download from claude.ai/download)
Python installed on your system

Installation

Clone the repository

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

Install the package
```
python setup.py
```

Configure Claude Desktop Add the following configuration to your Claude Desktop settings:

"mcpServers": {
  "data-exploration": {
    "command": "python",
    "args": ["-m", "mcp_server_ds"]
  }
}

Restart Claude Desktop After adding the configuration, restart Claude Desktop to load the MCP server.

Using Data Exploration Assistant

Start a new conversation in Claude Desktop
Select the explore-data prompt template This template will be available in the MCP templates section once the server is running.
Provide the required inputs:
- csv_path: Local path to your CSV file (e.g., "/Users/username/data/housing_data.csv")
- topic: The specific topic you want to explore (e.g., "Housing prices in California" or "Weather patterns in London")
Interact with your data The assistant will:
- Load and analyze your dataset
- Generate statistical summaries
- Create relevant visualizations
- Provide insights based on your specified topic

Example Workflow

Prepare your CSV file Ensure your CSV file is properly formatted with headers.
Start the exploration Input the file path and topic when prompted.
Review the analysis The assistant will provide:
- Dataset overview (size, structure, data types)
- Statistical summaries of key variables
- Visualizations of important relationships
- Insights relevant to your topic
- Potential areas for further investigation
Ask follow-up questions You can ask for more specific analyses or visualizations based on the initial findings.

Performance Considerations

The tool is optimized for large datasets (tested with files up to 200MB)
For very large files, initial loading may take a few moments
Complex visualizations on multi-million row datasets may require additional processing time

Troubleshooting

If you encounter issues:

Ensure your CSV file is properly formatted
Check that the file path is correct and accessible
Restart Claude Desktop if the MCP server connection is lost
Verify that your Python environment has the necessary dependencies