Database Analytics MCP Server

A practical database analytics MCP server built with FastMCP, providing SQLite database operations through the Model Context Protocol (MCP). Perfect for data analysis workflows with AI assistants like Claude.

What This Server Provides

🛠️ Tools (Interactive Operations)

• connect_db - Connect to SQLite database files
• execute_query - Execute SQL queries with safety checks
• list_tables - List all tables in the database

📊 Resources (Read-only Data Access)

• schema://tables/{table_name} - Get table column information and structure
• data://tables/{table_name} - Get sample table data for preview

🔐 Safety Features

• SQL injection prevention with dangerous operation blocking
• Read-only resource access for safe schema exploration
• Comprehensive error handling with descriptive messages

Setup and Installation

1. Clone the repository:

   git clone https://github.com/khuyentran1401/database-analytics-mcp.git
   cd database-analytics-mcp
   

2. Install UV (recommended):

   # Install UV if you haven't already
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

3. Install dependencies:

   # UV automatically manages dependencies from pyproject.toml
   uv sync
   

4. Test the server:

   uv run example_usage.py
   

5. Add to Claude Code

   # Quick setup
   claude mcp add database-analytics -- uv run database_mcp_server.py
   

   Verify installation:

   # Check if the server is registered
   claude mcp list
   

Other MCP Clients

For any MCP client that supports the standard protocol:

servers:
  - name: database-analytics
    transport:
      type: stdio
      command: uv
      args: ["run", "database_mcp_server.py"]

Example Workflows

Data Exploration with Claude Code

1. Connect to Database

   Connect to my SQLite database at ./sample_database.db
   

2. Explore Schema

   What tables are available in this database?
   

3. Examine Table Structure

   Show me the schema for the users table
   

4. Preview Data

   Show me some sample data from the users table
   

5. Run Analytics Queries

   Calculate total sales by product category
   

6. Export Results

   Export the query "SELECT product_name, SUM(quantity) as total_sold FROM orders GROUP BY product_name" to CSV file called sales_report.csv
   

7. Get Table Statistics

   Show me statistics for the users table
   

Common Use Cases

• Database Schema Discovery: Quickly understand database structure
• Data Quality Checks: Sample data to verify data integrity
• Ad-hoc Analysis: Run custom SQL queries safely
• Report Generation: Execute complex analytical queries
• Data Exploration: Browse tables and relationships with pagination
• Data Export: Export query results to CSV for external analysis
• Performance Monitoring: Track query execution times

Available Tools & Resources

Tools (Interactive Operations)

Tool	Parameters	Description
connect_db	database_path: str	Connect to SQLite database file
execute_query	sql: str	Execute SQL query with safety checks and timing
list_tables	None	List all tables in connected database
export_to_csv	sql: str, filename: str	Export query results to CSV file

Resources (Read-only Access)

Resource URI	Description
schema://tables/{table_name}	Get column info, types, constraints, foreign keys
data://tables/{table_name}	Get sample rows with pagination, statistics (limit, offset)
stats://tables/{table_name}	Get comprehensive table statistics and column analysis