MCP Data Exploration Server

An MCP (Model Context Protocol) server that provides dataset exploration and analysis tools for any LLM client. The server performs actual data analysis and returns formatted results, eliminating the need for users to write or execute code.

Features

• Dataset Analysis: Comprehensive summary, correlation analysis, distribution analysis, and missing value detection

• Data Cleaning: Automated cleaning operations with detailed results

• Statistical Testing: Normality tests, correlation significance tests, and t-tests

• MCP Compatible: Works with any MCP-compatible client (Claude Desktop, custom clients, etc.)

Related MCP server: mcp-csv-analyst

Quick Start

The fastest way to get started:

# 1. Navigate to project directory
cd /path/to/mcp-data-explore

# 2. Install dependencies
uv sync

# 3. Add MCP server to Claude Code (project scope - shared with team)
claude mcp add data-explore -s project -- uv --directory . run python main.py

# 4. Start Claude Code
claude

# 5. Try it out in Claude Code
# "Analyze the test_data.csv file"
# "What's the correlation between age and salary?"

Installation

Prerequisites

• Python 3.13 or higher

• uv package manager

Setup

1. Clone or download this repository:

git clone <repository-url>
cd mcp-data-explore

2. Install dependencies:

uv sync

3. Test the server:

python main.py

Connecting to MCP Clients

Claude Code

Claude Code has excellent built-in MCP support. Here's how to connect:

1. Project MCP Configuration (Recommended)

   The project already includes a .mcp.json file that's shared with everyone:

   {
     "mcpServers": {
       "data-explore": {
         "command": "uv",
         "args": [
           "--directory",
           ".",
           "run",
           "python",
           "main.py"
         ],
         "env": {}
       }
     }
   }

   This file is automatically detected by Claude Code when you start it in the project directory.

   Environment Variable Support in .mcp.json:

   You can use environment variables in your .mcp.json for flexibility:

   {
     "mcpServers": {
       "data-explore": {
         "command": "${UV_COMMAND:-uv}",
         "args": [
           "--directory",
           "${PROJECT_DIR:-.}",
           "run",
           "python",
           "main.py"
         ],
         "env": {
           "PYTHONPATH": "${CUSTOM_PYTHON_PATH:-}"
         }
       }
     }
   }

   Security Note: Claude Code will prompt for approval before using project-scoped servers from .mcp.json files for security.

2. Using Claude Code CLI to Add MCP Server

   You can add the MCP server using Claude Code CLI commands:

   # Add MCP server to project scope (shared with team via .mcp.json)
   claude mcp add data-explore -s project -- uv --directory . run python main.py
   
   # Add MCP server to user scope (available across all your projects)
   claude mcp add data-explore -s user -- uv --directory /Users/ida/Documents/eric/mcp-data-explore run python main.py
   
   # Add MCP server to local scope (private to you in this project) - DEFAULT
   claude mcp add data-explore -- uv --directory . run python main.py
   # or explicitly specify local scope
   claude mcp add data-explore -s local -- uv --directory . run python main.py
   
   # Add with environment variables if needed
   claude mcp add data-explore -s project -e PYTHONPATH=/custom/path -- uv --directory . run python main.py

   MCP Server Management Commands:

   # List all configured MCP servers
   claude mcp list
   
   # Get details for a specific server
   claude mcp get data-explore
   
   # Remove an MCP server
   claude mcp remove data-explore
   
   # Reset project-scoped server approval choices
   claude mcp reset-project-choices
   
   # Import servers from Claude Desktop (macOS/WSL only)
   claude mcp add-from-claude-desktop
   
   # Add server from JSON configuration
   claude mcp add-json data-explore '{"type":"stdio","command":"uv","args":["--directory",".","run","python","main.py"],"env":{}}'

3. Understanding MCP Server Scopes

   Claude Code supports three MCP server scopes with clear precedence:

   • local (default): Private to you in current project only

   • project: Shared with team via .mcp.json file (version controlled)

   • user: Available to you across all projects on your machine

   Scope Precedence: local > project > user (local overrides project, project overrides user)

   Choosing the Right Scope:

   • Local: Experimental configurations, sensitive credentials, personal development

   • Project: Team-shared tools, project-specific services, collaboration requirements

   • User: Personal utilities, development tools, cross-project services

   # View all MCP commands and help
   claude mcp --help
   
   # Check connection status of all servers (use /mcp command in Claude Code)
   /mcp
   
   # Configure server startup timeout (10 seconds example)
   MCP_TIMEOUT=10000 claude

4. Start Claude Code

   # Start in project directory (automatically loads .mcp.json)
   cd /Users/ida/Documents/eric/mcp-data-explore
   claude
   
   # Claude Code will automatically detect and load the project MCP configuration
   # You can use the /mcp command within Claude Code to check server status

5. Verify Connection

   Once connected, you should see the MCP tools available. Try asking:

   • "What MCP tools are available?"

   • "What MCP servers are connected?"

   • "Analyze the test_data.csv file"

   • "Show me the dataset summary for test_data.csv"

6. Usage Examples with Claude Code

   You: "Analyze the test dataset in this directory"
   Claude Code: [Uses analyze_dataset tool] → Returns comprehensive analysis
   
   You: "What's the correlation between age and salary?"
   Claude Code: [Uses analyze_dataset with correlation type] → Returns correlation matrix
   
   You: "Clean my data by removing duplicates and filling nulls"
   Claude Code: [Uses clean_data tool] → Returns cleaning results
   
   You: "Test if the age column is normally distributed"
   Claude Code: [Uses statistical_summary tool] → Returns normality test results

Claude Desktop

1. Install Claude Desktop

   • Download from claude.ai/download

   • Make sure you have the latest version

2. Configure Claude Desktop

   Open your Claude Desktop configuration file:

   macOS/Linux:

   code ~/Library/Application\ Support/Claude/claude_desktop_config.json

   Windows:

   code %APPDATA%\Claude\claude_desktop_config.json

3. Add Server Configuration

   Add the following to your claude_desktop_config.json:

   {
     "mcpServers": {
       "data-explore": {
         "command": "uv",
         "args": [
           "--directory", 
           "/ABSOLUTE/PATH/TO/mcp-data-explore",
           "run", 
           "python", 
           "main.py"
         ]
       }
     }
   }

   Important: Replace /ABSOLUTE/PATH/TO/mcp-data-explore with the actual absolute path to your project directory.

   Windows Example:

   {
     "mcpServers": {
       "data-explore": {
         "command": "uv",
         "args": [
           "--directory", 
           "C:\\\\Users\\\\YourName\\\\mcp-data-explore",
           "run", 
           "python", 
           "main.py"
         ]
       }
     }
   }

4. Restart Claude Desktop

   Completely close and restart Claude Desktop for the changes to take effect.

5. Verify Connection

   Look for the tools icon in Claude Desktop. You should see 3 available tools:

   • analyze_dataset

   • clean_data

   • statistical_summary

Other MCP Clients

For other MCP-compatible clients, use these connection details:

• Transport: stdio

• Command: uv --directory /path/to/mcp-data-explore run python main.py

• Server Name: data-explore

Available Tools

1. analyze_dataset

Performs comprehensive dataset analysis.

Parameters:

• dataset_path (required): Path to CSV file

• analysis_type (optional): "summary", "correlation", "distribution", "missing_values" (default: "summary")

• columns (optional): List of column names to analyze

Example Usage:

• "Analyze the dataset at /path/to/data.csv"

• "Show correlation analysis for the sales data"

• "Check for missing values in my dataset"

2. clean_data

Performs data cleaning operations and shows results.

Parameters:

• dataset_path (required): Path to CSV file

• operations (required): List of operations - "remove_nulls", "fill_nulls", "remove_duplicates", "standardize_columns", "convert_types"

• output_path (optional): Path to save cleaned dataset

Example Usage:

• "Clean my dataset by removing null values and duplicates"

• "Fill missing values and standardize column names"

• "Optimize data types in my dataset"

3. statistical_summary

Performs statistical tests and analysis.

Parameters:

• dataset_path (required): Path to CSV file

• columns (optional): Specific columns to analyze

• tests (optional): List of tests - "normality", "correlation_test", "ttest"

Example Usage:

• "Run statistical tests on my dataset"

• "Test if the age column follows a normal distribution"

• "Check correlation significance between variables"

Example Usage

Once connected to your MCP client, you can ask natural language questions like:

• "Analyze the dataset at /Users/me/sales_data.csv"

• "What's the correlation between age and income in my data?"

• "Clean my dataset by removing duplicates and filling missing values"

• "Test if the revenue column is normally distributed"

• "Show me distribution analysis for the price column"

The server will automatically:

1. Load your CSV data

2. Perform the requested analysis

3. Return formatted results with insights and interpretations

Troubleshooting

Claude Code MCP Issues

1. MCP Server Not Loading

   # Verify MCP server starts manually
   cd /Users/ida/Documents/eric/mcp-data-explore
   python main.py
   
   # Check server configuration
   claude mcp get data-explore
   
   # List all servers
   claude mcp list

2. Tools Not Available

   • Ensure .mcp.json is in the project root for project scope

   • Check JSON syntax is valid (use claude mcp get data-explore)

   • Use /mcp command in Claude Code to check connection status

   • Try: "What MCP servers are connected?" or "What MCP tools are available?"

3. Path Issues

   • Use relative paths ("--directory", ".") for project scope

   • Use absolute paths for user scope

   • Ensure uv is in your PATH: which uv

   • For Windows: May need cmd /c wrapper for some commands

4. Security Approval Required

   • Claude Code prompts for approval before using project-scoped servers

   • Click "Allow" when prompted

   • Use claude mcp reset-project-choices to reset approval choices

5. Debug MCP Connection

   # Test server directly with JSON-RPC
   echo '{"jsonrpc": "2.0", "method": "initialize", "params": {}, "id": 1}' | python main.py
   
   # Set debug timeout
   MCP_TIMEOUT=30000 claude

Server Not Appearing in Claude Desktop

1. Check your JSON syntax in claude_desktop_config.json

2. Ensure the path is absolute (not relative)

3. Restart Claude Desktop completely

4. Check Claude's logs: ~/Library/Logs/Claude/mcp*.log

Tool Calls Failing

1. Verify the CSV file path exists and is accessible

2. Check that the CSV file is properly formatted

3. Ensure all required parameters are provided

4. Look for error messages in the returned results

Import Errors

If you see module import errors:

uv sync  # Reinstall dependencies
python -c "import pandas; print('Dependencies OK')"  # Test imports

Development

Adding New Tools

1. Add a new @mcp.tool() decorated async function in main.py

2. Follow the existing pattern for error handling and input validation

3. Update CLAUDE.md with the new tool specifications

4. Test the tool before deploying

Extending Data Format Support

Currently supports CSV files. To add support for other formats:

1. Modify the data loading logic in each tool

2. Add format detection based on file extension

3. Update tool documentation and examples

License

This project is open source. Feel free to modify and distribute according to your needs.