Quick Data

This MCP server enables Windows users to run a data analytics platform that ingests CSV/JSON files, discovers data schemas, runs 32+ analytics tools, and renders interactive visualizations. It is configured to integrate with Claude Desktop for streamlined command execution and data workflows on Windows machines.

How to use

You interact with the Quick Data MCP server through Claude Desktop. Start the server inside Claude Desktop and then issue commands to load datasets, run analyses, and visualize results. Typical workflows include loading a CSV or JSON file, performing correlations or distributions analyses, and creating charts or dashboards.

Suggested usage patterns you can run in Claude Desktop after setup:

• Load a dataset: Load my sales data: C:\Users\YourName\Documents\sales_data.csv as "sales"
• Basic analysis: Show me correlations in the sales dataset
• Visualization: Create a bar chart of sales by region
• Advanced analytics: Generate dashboard with revenue trends and regional breakdown

How to install

# Prerequisites
- Windows 10/11
- Python 3.9+
- Claude Desktop

# 1) Download or clone the project
# (Use the exact commands shown here to reproduce the setup)
git clone https://github.com/Beaulewis1977/quick-data-for-windows-mcp.git
cd quick-data-for-windows-mcp

# 2) Install dependencies
install_dependencies.bat

# 3) Test the server standalone (optional but recommended)
test_server.bat

# 4) Configure Claude Desktop
copy claude_desktop_config_fixed.json "%APPDATA%\Claude\claude_desktop_config.json"

# 5) Edit the config to point to your installation (cwd) if needed
"IMPORTANT" Edit the config file and update the cwd path to your actual installation directory.

# 6) Restart Claude Desktop

Configuration and troubleshooting notes

If you need manual configuration, you can place a runtime configuration in Claude Desktop to point to the local Python script. The server is started via Python and expects the working directory to be the project path.

{
  "mcpServers": {
    "quick_data_win": {
      "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"
      }
    }
  }
}

Troubleshooting and testing

If you encounter ModuleNotFoundError: No module named 'mcp', verify Python is accessible and dependencies are installed. Ensure Python is in your PATH and that the required packages are present.

To test the server independently of Claude Desktop, run:

• python main.py Expected output:

Server running on stdio...```

## Available tools

### load\_dataset

Load CSV/JSON files with automatic schema discovery into memory for analysis.

### list\_loaded\_datasets

List all datasets currently loaded in memory.

### get\_dataset\_info

Retrieve detailed information about a loaded dataset.

### clear\_dataset

Remove a specific dataset from memory.

### clear\_all\_datasets

Clear all loaded datasets from memory.

### segment\_by\_column

Analyze data by categorical segments.

### find\_correlations

Compute relationships between variables across datasets.

### analyze\_distributions

Analyze statistical distributions of numeric columns.

### detect\_outliers

Identify anomalous data points.

### suggest\_analysis

AI-powered recommendations for additional analyses.

### create\_chart

Generate an interactive chart (bar, scatter, line, histogram).

### generate\_dashboard

Create a multi-chart dashboard from multiple datasets.

### validate\_data\_quality

Score data quality across defined criteria.

### compare\_datasets

Compare two datasets to highlight differences.

### merge\_datasets

Join datasets with flexible strategies.

### calculate\_feature\_importance

Analyze which features contribute most to a target variable.

### export\_insights

Export results in multiple formats (CSV/JSON/PNG/HTML).