Quick Data for Windows MCP

Windows-optimized fork of disler/quick-data-mcp for Claude Desktop

Universal data analytics capabilities for JSON/CSV files - now working seamlessly on Windows!

🚀 What This Does

This is a Windows-optimized fork of the excellent quick-data-mcp project by @disler.

The original project provides powerful MCP server capabilities for data analytics, and this fork specifically addresses Windows compatibility issues and Claude Desktop integration challenges.

**This is my first ever try at this. Please feel free to give suggestions and or criticisms. I loved the quick data mcp for claude code. There was nothing available like it for claude desktop so with the help of claude code we now have it.

Key Improvements Over Original:

✅ Windows Path Handling - Proper Windows file path support
✅ Claude Desktop Ready - Pre-configured batch launchers and setup
✅ Dependency Management - Automated installation scripts
✅ Troubleshooting - Complete guides for common Windows issues

✨ Key Features

Universal Data Support - Works with any CSV/JSON file structure
Windows Path Optimization - Handles Windows file paths correctly
Claude Desktop Integration - Pre-configured for seamless setup
Automatic Schema Discovery - Analyzes your data and suggests analyses
32+ Analytics Tools - From basic stats to advanced ML features
Interactive Visualizations - Create charts with Plotly
Memory Management - Optimized for large datasets

🏁 Quick Start for Windows

Prerequisites

Windows 10/11
Python 3.9+ (Download here)
Claude Desktop (Download here)

Installation

Download or clone this repository:

git clone https://github.com/Beaulewis1977/quick-data-for-windows-mcp.git
cd quick-data-for-windows-mcp

Install dependencies:
```
install_dependencies.bat
```
Test the server:
```
test_server.bat
```
Configure Claude Desktop:

Copy the fixed configuration to Claude Desktop:
```
copy claude_desktop_config_fixed.json "%APPDATA%\Claude\claude_desktop_config.json"
```
IMPORTANT: Edit the config file and update the cwd path to your actual installation directory.
Restart Claude Desktop

🚨 Having Issues?

If you see ModuleNotFoundError: No module named 'mcp', check the TROUBLESHOOTING.md guide.

💻 Usage in Claude Desktop

Once configured, start with this slash command in Claude Desktop:

/quick-data-windows

Loading Your Data

Load my sales data: C:\Users\YourName\Documents\sales_data.csv as "sales"

Basic Analysis

Show me correlations in the sales dataset
Create a bar chart of sales by region
Analyze the distribution of revenue column

Advanced Analytics

Validate data quality for sales dataset
Compare sales dataset with marketing dataset
Generate dashboard with revenue trends and regional breakdown

🔧 Available Tools

Dataset Management

load_dataset - Load CSV/JSON files with automatic schema discovery
list_loaded_datasets - View all datasets in memory
get_dataset_info - Get detailed dataset information
clear_dataset / clear_all_datasets - Memory management

Core Analytics

segment_by_column - Analyze categorical data segments
find_correlations - Discover relationships between variables
analyze_distributions - Statistical distribution analysis
detect_outliers - Identify data anomalies
suggest_analysis - AI-powered analysis recommendations

Visualization

create_chart - Generate interactive charts (bar, scatter, line, histogram)
generate_dashboard - Multi-chart dashboards

Advanced Analytics

validate_data_quality - Comprehensive data quality scoring
compare_datasets - Multi-dataset comparison analysis
merge_datasets - Join datasets with flexible strategies
calculate_feature_importance - ML feature importance analysis
export_insights - Export results in multiple formats

📂 Supported File Formats

CSV Files

Standard CSV with headers
Custom delimiters automatically detected
UTF-8 encoding support
Large file handling with sampling options

JSON Files

Flat JSON structures
Nested JSON (automatically flattened)
JSON Lines format
Array of objects format

🛠️ Configuration

Manual Configuration

If the automatic setup doesn't work, manually edit your Claude Desktop config:

Location: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "quick-data-windows": {
      "command": "python",
      "args": [
        "C:\\path\\to\\your\\quick-data-for-windows-mcp\\main.py"
      ],
      "cwd": "C:\\path\\to\\your\\quick-data-for-windows-mcp",
      "env": {
        "LOG_LEVEL": "INFO",
        "PYTHONPATH": "C:\\path\\to\\your\\quick-data-for-windows-mcp\\src"
      }
    }
  }
}

Alternative: Using UV Package Manager

If you prefer UV (recommended for Python dependency management):

{
  "mcpServers": {
    "quick-data-windows": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\path\\to\\your\\quick-data-for-windows-mcp",
        "run",
        "python",
        "main.py"
      ]
    }
  }
}

🧪 Testing the Server

Test the server standalone (before Claude Desktop integration):

python main.py

Expected output:

Quick Data for Windows MCP v1.0.0
Server running on stdio...

📊 Example Workflows

Sales Data Analysis

1. Load sales_data.csv as "sales"
2. Show correlations in sales dataset
3. Create bar chart of sales by product_category 
4. Detect outliers in revenue column
5. Generate dashboard with top products and regional trends

Data Quality Assessment

1. Load customer_data.csv as "customers"
2. Validate data quality for customers dataset
3. Analyze distributions for age column
4. Segment by customer_type column

🔍 Troubleshooting

Common Issues

"Module not found" errors:

Ensure Python is in your PATH
Run pip install -r requirements.txt manually
Check that PYTHONPATH is set correctly in config

"File not found" errors:

Use full Windows paths: C:\Users\...
Avoid relative paths like .\data\file.csv
Check file permissions

Claude Desktop not finding server:

Restart Claude Desktop after config changes
Check config file syntax with JSON validator
Verify file paths are correct (no typos)

Getting Help

Check that Python 3.9+ is installed: python --version
Verify dependencies: pip list | findstr pandas
Test server manually: python main.py
Check Claude Desktop logs for errors

🤝 Contributing

This is a community-driven Windows adaptation of the original quick-data-mcp project. Contributions welcome!

Development Setup

# Clone and setup
git clone https://github.com/Beaulewis1977/quick-data-for-windows-mcp.git
cd quick-data-for-windows-mcp

# Install development dependencies
pip install -r requirements.txt
pip install pytest black ruff

# Run tests (when implemented)
pytest tests/

📝 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

This project is a Windows-optimized fork of the original quick-data-mcp by @disler.

Original Project Credits

Original Author: @disler
Original Repository: disler/quick-data-mcp
Original Purpose: MCP server for data analytics with Claude Code
License: MIT (maintained in this fork)

Windows Fork Contributions

Windows Compatibility: @Beaulewis1977
Claude Desktop Integration: Community-driven improvements
Troubleshooting & Documentation: Enhanced for Windows users

Technology Stack

Model Context Protocol: Anthropic
Data Processing: pandas, numpy, plotly, scikit-learn
Platform: Optimized for Windows + Claude Desktop

⭐ Please star both repositories:

Original Project - For the core innovation
This Fork - For Windows support

Special thanks to @disler for creating the foundational work that made this Windows adaptation possible!

🔗 Links

Ready to analyze your data with AI? Load a CSV and start exploring! 🚀

Quick Data for Windows MCP

Windows-optimized fork of disler/quick-data-mcp for Claude Desktop

Universal data analytics capabilities for JSON/CSV files - now working seamlessly on Windows!

🚀 What This Does

This is a Windows-optimized fork of the excellent quick-data-mcp project by @disler.

The original project provides powerful MCP server capabilities for data analytics, and this fork specifically addresses Windows compatibility issues and Claude Desktop integration challenges.

Key Improvements Over Original:

✅ Windows Path Handling - Proper Windows file path support
✅ Claude Desktop Ready - Pre-configured batch launchers and setup
✅ Dependency Management - Automated installation scripts
✅ Troubleshooting - Complete guides for common Windows issues

✨ Key Features

Universal Data Support - Works with any CSV/JSON file structure
Windows Path Optimization - Handles Windows file paths correctly
Claude Desktop Integration - Pre-configured for seamless setup
Automatic Schema Discovery - Analyzes your data and suggests analyses
32+ Analytics Tools - From basic stats to advanced ML features
Interactive Visualizations - Create charts with Plotly
Memory Management - Optimized for large datasets

🏁 Quick Start for Windows

Prerequisites

Windows 10/11
Python 3.9+ (Download here)
Claude Desktop (Download here)

Installation

Download or clone this repository:

git clone https://github.com/Beaulewis1977/quick-data-for-windows-mcp.git
cd quick-data-for-windows-mcp

Install dependencies:
```
install_dependencies.bat
```
Test the server:
```
test_server.bat
```
Configure Claude Desktop:

Copy the fixed configuration to Claude Desktop:
```
copy claude_desktop_config_fixed.json "%APPDATA%\Claude\claude_desktop_config.json"
```
IMPORTANT: Edit the config file and update the cwd path to your actual installation directory.
Restart Claude Desktop

🚨 Having Issues?

If you see ModuleNotFoundError: No module named 'mcp', check the TROUBLESHOOTING.md guide.

💻 Usage in Claude Desktop

Once configured, start with this slash command in Claude Desktop:

/quick-data-windows

Loading Your Data

Load my sales data: C:\Users\YourName\Documents\sales_data.csv as "sales"

Basic Analysis

Show me correlations in the sales dataset
Create a bar chart of sales by region
Analyze the distribution of revenue column

Advanced Analytics

Validate data quality for sales dataset
Compare sales dataset with marketing dataset
Generate dashboard with revenue trends and regional breakdown

🔧 Available Tools

Dataset Management

load_dataset - Load CSV/JSON files with automatic schema discovery
list_loaded_datasets - View all datasets in memory
get_dataset_info - Get detailed dataset information
clear_dataset / clear_all_datasets - Memory management

Core Analytics

segment_by_column - Analyze categorical data segments
find_correlations - Discover relationships between variables
analyze_distributions - Statistical distribution analysis
detect_outliers - Identify data anomalies
suggest_analysis - AI-powered analysis recommendations

Visualization

create_chart - Generate interactive charts (bar, scatter, line, histogram)
generate_dashboard - Multi-chart dashboards

Advanced Analytics

validate_data_quality - Comprehensive data quality scoring
compare_datasets - Multi-dataset comparison analysis
merge_datasets - Join datasets with flexible strategies
calculate_feature_importance - ML feature importance analysis
export_insights - Export results in multiple formats

📂 Supported File Formats

CSV Files

Standard CSV with headers
Custom delimiters automatically detected
UTF-8 encoding support
Large file handling with sampling options

JSON Files

Flat JSON structures
Nested JSON (automatically flattened)
JSON Lines format
Array of objects format

🛠️ Configuration

Manual Configuration

If the automatic setup doesn't work, manually edit your Claude Desktop config:

Location: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "quick-data-windows": {
      "command": "python",
      "args": [
        "C:\\path\\to\\your\\quick-data-for-windows-mcp\\main.py"
      ],
      "cwd": "C:\\path\\to\\your\\quick-data-for-windows-mcp",
      "env": {
        "LOG_LEVEL": "INFO",
        "PYTHONPATH": "C:\\path\\to\\your\\quick-data-for-windows-mcp\\src"
      }
    }
  }
}

Alternative: Using UV Package Manager

If you prefer UV (recommended for Python dependency management):

{
  "mcpServers": {
    "quick-data-windows": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\path\\to\\your\\quick-data-for-windows-mcp",
        "run",
        "python",
        "main.py"
      ]
    }
  }
}

🧪 Testing the Server

Test the server standalone (before Claude Desktop integration):

python main.py

Expected output:

Quick Data for Windows MCP v1.0.0
Server running on stdio...

📊 Example Workflows

Sales Data Analysis

1. Load sales_data.csv as "sales"
2. Show correlations in sales dataset
3. Create bar chart of sales by product_category 
4. Detect outliers in revenue column
5. Generate dashboard with top products and regional trends

Data Quality Assessment

1. Load customer_data.csv as "customers"
2. Validate data quality for customers dataset
3. Analyze distributions for age column
4. Segment by customer_type column

🔍 Troubleshooting

Common Issues

"Module not found" errors:

Ensure Python is in your PATH
Run pip install -r requirements.txt manually
Check that PYTHONPATH is set correctly in config

"File not found" errors:

Use full Windows paths: C:\Users\...
Avoid relative paths like .\data\file.csv
Check file permissions

Claude Desktop not finding server:

Restart Claude Desktop after config changes
Check config file syntax with JSON validator
Verify file paths are correct (no typos)

Getting Help

Check that Python 3.9+ is installed: python --version
Verify dependencies: pip list | findstr pandas
Test server manually: python main.py
Check Claude Desktop logs for errors

🤝 Contributing

This is a community-driven Windows adaptation of the original quick-data-mcp project. Contributions welcome!

Development Setup

# Clone and setup
git clone https://github.com/Beaulewis1977/quick-data-for-windows-mcp.git
cd quick-data-for-windows-mcp

# Install development dependencies
pip install -r requirements.txt
pip install pytest black ruff

# Run tests (when implemented)
pytest tests/

📝 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

This project is a Windows-optimized fork of the original quick-data-mcp by @disler.

Original Project Credits

Original Author: @disler
Original Repository: disler/quick-data-mcp
Original Purpose: MCP server for data analytics with Claude Code
License: MIT (maintained in this fork)

Windows Fork Contributions

Windows Compatibility: @Beaulewis1977
Claude Desktop Integration: Community-driven improvements
Troubleshooting & Documentation: Enhanced for Windows users

Technology Stack

Model Context Protocol: Anthropic
Data Processing: pandas, numpy, plotly, scikit-learn
Platform: Optimized for Windows + Claude Desktop

⭐ Please star both repositories:

Original Project - For the core innovation
This Fork - For Windows support

Special thanks to @disler for creating the foundational work that made this Windows adaptation possible!

🔗 Links

Ready to analyze your data with AI? Load a CSV and start exploring! 🚀