mcp-csv-database 0.1.2

MCP server for CSV file management and SQL querying

MCP CSV Database Server

A Model Context Protocol (MCP) server that provides tools for loading CSV files into a temporary SQLite database and executing SQL queries on the data.

Features

• Load CSV files: Automatically detect CSV separators and load multiple files from a folder
• SQL queries: Execute any SQL query on loaded data with result formatting
• Schema inspection: View database schema and table structures
• Data analysis: Built-in tools for data exploration and analysis
• Export capabilities: Export query results or tables back to CSV
• Performance tools: Create indexes and analyze query execution plans

Installation

From PyPI

pip install mcp-csv-database

From source

git clone https://github.com/Lasitha-Jayawardana/mcp-csv-database.git
cd mcp-csv-database
pip install -e .

Usage

Command Line

Start the server with stdio transport:

mcp-csv-database

Or auto-load CSV files from a folder:

mcp-csv-database --csv-folder /path/to/csv/files

Configuration

Add to your MCP client configuration:

{
  "mcpServers": {
    "csv-database": {
      "command": "mcp-csv-database",
      "args": ["--csv-folder", "/path/to/your/csv/files"]
    }
  }
}

Available Tools

Data Loading

• load_csv_folder(folder_path, table_prefix="") - Load all CSV files from a folder
• list_loaded_tables() - List currently loaded tables
• clear_database() - Clear all loaded data

Data Querying

• execute_sql_query(query, limit=100) - Execute SQL queries with automatic result formatting
• get_database_schema() - View complete database schema
• get_table_info(table_name) - Get detailed information about a specific table

Data Analysis

• get_query_plan(query) - Analyze query execution plans
• create_index(table_name, column_name, index_name="") - Create indexes for better performance

Data Export

• export_table_to_csv(table_name, output_path, include_header=True) - Export tables to CSV
• backup_database(backup_path) - Create database backups

Examples

Basic Usage

# Load CSV files
result = load_csv_folder("/path/to/csv/files")

# View what's loaded
schema = get_database_schema()

# Query the data
result = execute_sql_query("SELECT * FROM my_table LIMIT 10")

# Export results
export_table_to_csv("my_table", "/path/to/output.csv")

Data Analysis

# Get table information
info = get_table_info("sales_data")

# Analyze data
result = execute_sql_query("""
    SELECT 
        category,
        COUNT(*) as count,
        AVG(price) as avg_price,
        SUM(quantity) as total_quantity
    FROM sales_data 
    GROUP BY category
    ORDER BY total_quantity DESC
""")

# Create index for better performance
create_index("sales_data", "category")

Transport Options

The server supports multiple transport methods:

• stdio (default): Standard input/output
• sse: Server-sent events
• streamable-http: HTTP streaming

# SSE transport
mcp-csv-database --transport sse --port 8080

# HTTP transport  
mcp-csv-database --transport streamable-http --port 8080

Requirements

• Python 3.8+
• pandas
• sqlite3 (built-in)
• mcp

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

v0.1.0

• Initial release
• Basic CSV loading and SQL querying
• Schema inspection tools
• Data export capabilities
• Multiple transport support