Data Analyst Server

Transform Claude (or ChatGPT) into a powerful data analyst with SQL capabilities. Import CSVs or Google Sheets, run complex queries, and get instant insights - all through natural language.

🎯 What This Does

This MCP server lets AI assistants analyze your data by:

• Importing CSV files and Google Sheets
• Querying data with full SQL (JOINs, aggregations, window functions)
• Analyzing datasets from 100 rows to billions
• Exporting results to CSV

Simple Example:

You: "Load sales_2024.csv as sales and show me the top 10 products by revenue"

Claude/ChatGPT:
1. Imports your CSV into a DuckDB database
2. Writes SQL: SELECT product, SUM(revenue) FROM sales GROUP BY product ORDER BY revenue DESC LIMIT 10
3. Executes the query
4. Shows results with insights: "Your top product is Widget A with $50K revenue..."

🚀 Quick Start

Prerequisites

• Python 3.8+ (or Docker Desktop)
• Claude Desktop or ChatGPT Desktop
• 2GB+ RAM

Installation

Option 1: Docker (Recommended)

# Clone or download this repo
cd mcp-data-analyst

# Start the container
./docker-start.sh

# Falls das File nicht ausführbar ist. führe im Terminal folgenden Befehl aus:

chmod +x docker-start.sh

# Danach starte es erneut:

./docker-start.sh 


# Follow the instructions to configure Claude/ChatGPT

Option 2: Direct Python Install

# Clone or download this repo
cd mcp-data-analyst

# Install dependencies
pip install -r requirements.txt

# Run the server
python server.py

📋 Configuration

Step 1: Configure Your AI Assistant

For Claude Desktop

Edit the config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

If using Docker:

{
  "mcpServers": {
    "mcp-data-analyst": {
      "command": "docker",
      "args": ["exec", "-i", "data-analyst-mcp", "python3", "server.py"]
    }
  }
}

If using direct install:

{
  "mcpServers": {
    "mcp-data-analyst": {
      "command": "python3",
      "args": ["/absolute/path/to/mcp-data-analyst/server.py"]
    }
  }
}

For ChatGPT Desktop

Edit the config file:

• macOS: ~/Library/Application Support/ChatGPT/config.json
• Windows: %APPDATA%\ChatGPT\config.json

Use the same JSON format as Claude Desktop above.

Step 2: Configure Environment (Optional)

For cloud storage with MotherDuck (handles billions of rows):

# Copy the example
cp .env.example .env

# Edit .env and add your token
nano .env

Add your MotherDuck token:

MOTHERDUCK_TOKEN=your_token_here
MEMORY_LIMIT=4G
CPU_LIMIT=2.0

Get a free token at motherduck.com (10GB free tier).

Without MotherDuck: Data is stored in-memory (fast, but session-only).

Step 3: Restart Your AI Assistant

Completely quit and restart Claude Desktop or ChatGPT Desktop.

Step 4: Test It!

You: "Load example_data.csv as customers and show me the data"

The AI will import the file and show you the results!

📊 Features

6 Powerful Tools

1. import_csv - Load CSV files, Google Sheets, or URLs
2. query_data - Execute SQL queries with full DuckDB support
3. list_tables - Show all available tables
4. describe_table - Get schema and sample data
5. export_query_results - Save query results to CSV
6. get_table_stats - Get statistical summaries

Supported Data Sources

• Local CSV files - Any CSV on your computer
• CSV URLs - Direct HTTP/HTTPS links
• Google Sheets - Automatically converts share links to CSV
• Multiple files - Load multiple CSVs as separate tables

SQL Capabilities

Full SQL support including:

• SELECT, WHERE, GROUP BY, ORDER BY, LIMIT
• JOINs (INNER, LEFT, RIGHT, FULL OUTER)
• Aggregate functions (SUM, AVG, COUNT, MIN, MAX)
• Window functions (ROW_NUMBER, RANK, LAG, LEAD)
• CTEs (WITH clauses)
• Subqueries
• Date/time functions

Data Size Limits

Rows	CSV Size	Mode	Performance
< 1M	~100MB	In-memory	⚡ Instant
1-10M	~1GB	In-memory	✅ Fast (seconds)
10M+	1GB+	MotherDuck	☁️ Optimized (cloud)

Recommendation: Use in-memory for < 10M rows, MotherDuck for larger datasets.

💡 Usage Examples

Example 1: Basic Analysis

You: "Load my sales data from https://example.com/sales.csv as sales"
AI: ✓ Imported 50,000 rows into 'sales' table

You: "What are the top 5 products by revenue?"
AI: [Writes and executes SQL, shows results with insights]

You: "Show me monthly revenue trends"
AI: [Creates time-series analysis with DATE_TRUNC]

Example 2: Multi-Table Analysis

You: "Load sales.csv as sales and products.csv as products"
AI: ✓ Imported both tables

You: "Join these tables and show me which product categories generate the most revenue"
AI: [Automatically identifies the relationship (product_id), performs JOIN, provides analysis]

Example 3: Advanced Analytics

You: "Analyze customer behavior and create segments based on purchase patterns"
AI: [Performs multi-step analysis with CTEs, window functions, and provides business insights]

Example 4: Google Sheets

You: "Load this Google Sheet: https://docs.google.com/spreadsheets/d/abc123/edit"
AI: ✓ Converted to CSV and imported

You: "Calculate the correlation between marketing spend and sales"
AI: [Performs statistical analysis]

🐳 Docker Commands

# Start server
./docker-start.sh

# Stop server
./docker-stop.sh

# View logs
docker-compose logs -f

# Restart
docker-compose restart

# Stop and remove
docker-compose down

# Rebuild
docker-compose down && docker-compose up -d --build

🔧 Troubleshooting

MCP Server Not Appearing

1. Verify config file path is correct
2. Use absolute paths (not ~ or relative)
3. Ensure container is running: docker ps
4. Check logs: docker-compose logs
5. Restart AI assistant completely (quit, not just close)

Permission Denied on Scripts

chmod +x docker-start.sh docker-stop.sh setup.sh

Import Errors

• Local files: Use absolute paths
• Google Sheets: Share with "Anyone with link"
• URLs: Verify URL is publicly accessible

Out of Memory

For large datasets (> 10M rows):

Option 1: Increase Docker memory in docker-compose.yml:

memory: 8G  # or 16G
cpus: '4.0'

Option 2: Use MotherDuck (recommended for > 10M rows):

1. Get token from motherduck.com
2. Add to .env: MOTHERDUCK_TOKEN=your_token
3. Restart: docker-compose restart

🏗️ Architecture

┌─────────────────────────────────────┐
│   Claude Desktop / ChatGPT Desktop  │
│   (Natural Language Interface)      │
└─────────────────┬───────────────────┘
                  │ MCP Protocol
                  ▼
┌─────────────────────────────────────┐
│   MCP Data Analyst Server (Python) │
│   - 6 Tools (FastMCP)               │
│   - CSV Import & SQL Query          │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│   DuckDB Database                   │
│   - In-Memory (< 10M rows)          │
│   - MotherDuck Cloud (billions)     │
└─────────────────────────────────────┘

🔒 Security & Privacy

• In-memory mode: All data stays on your computer, never leaves your machine
• MotherDuck mode: Data stored in your private cloud account
• No external sharing: Your data is never sent to third parties
• Read-only SQL: Only SELECT queries, no data modification
• Container isolation: Docker provides additional security layer

📚 Advanced Configuration

Custom Memory Limits

Edit docker-compose.yml:

deploy:
  resources:
    limits:
      memory: 8G      # Increase for large datasets
      cpus: '4.0'     # More CPU for faster processing

Multiple Tables

Load multiple CSVs:

You: "Load sales.csv, products.csv, and customers.csv"
AI: [Imports all three as separate tables]

You: "Show me how these tables relate to each other"
AI: [Analyzes schemas, identifies foreign keys, suggests JOINs]

Export Results

You: "Export the top 100 customers to a CSV file"
AI: [Executes query and saves to /data/top_customers.csv]

🛠️ Development

Adding Custom Tools

The FastMCP framework makes it easy to add new tools:

@mcp.tool()
def my_custom_tool(param: str, count: int = 10) -> dict:
    """Description that appears to the AI"""
    # Your logic here
    return {"result": "..."}

That's it! FastMCP handles validation, errors, and protocol details.

Project Structure

mcp-data-analyst/
├── server.py              # Main MCP server (FastMCP)
├── requirements.txt       # Python dependencies
├── Dockerfile            # Container definition
├── docker-compose.yml    # Docker orchestration
├── docker-start.sh       # Easy start script
├── docker-stop.sh        # Easy stop script
├── setup.sh              # Direct install script
├── .env.example          # Environment template
├── .gitignore            # Git ignore rules
└── example_data.csv      # Sample data for testing

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

📄 License

MIT License - Free to use, modify, and distribute.

🙏 Credits

Built with:

• FastMCP - Modern MCP framework
• DuckDB - Fast analytical database
• MotherDuck - Cloud data warehouse
• MCP Protocol by Anthropic

📞 Support

• Issues: Open a GitHub issue
• Questions: Start a discussion
• Documentation: Check this README

🌟 Star This Repo

If you find this useful, please star the repo! ⭐

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Transform your AI assistant into a data analyst in 5 minutes! 📊✨