MCP Database Utilities

中文文档

Overview

MCP Database Utilities is a unified database access service that supports multiple database types (PostgreSQL and SQLite). Through its abstraction layer design, it provides a simple and unified database operation interface for MCP servers.

Features

• Unified database access interface
• Support for multiple database configurations
• Secure read-only query execution
• Table structure and schema information retrieval
• Database tables listing via MCP tools
• Intelligent connection management and resource cleanup
• Debug mode support

Installation and Configuration

Installation Methods

Installing via Smithery

To install Database Utilities for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @donghao1393/mcp-dbutils --client claude

Using uvx (Recommended)

No installation required, run directly using uvx:

uvx mcp-dbutils --config /path/to/config.yaml

Add to Claude configuration:

"mcpServers": {
  "mcp-dbutils": {
    "command": "uvx",
    "args": [
      "mcp-dbutils",
      "--config",
      "/path/to/config.yaml"
    ],
    "env": {
      "MCP_DEBUG": "1"  // Optional: Enable debug mode
    }
  }
}

Using pip

pip install mcp-dbutils

Add to Claude configuration:

            ```json

"mcpServers": { "mcp-dbutils": { "command": "python", "args": [ "-m", "mcp_dbutils", "--config", "/path/to/config.yaml" ], "env": { "MCP_DEBUG": "1" // Optional: Enable debug mode } } }

#### Using Docker
```bash
docker run -i --rm \
  -v /path/to/config.yaml:/app/config.yaml \
  -v /path/to/sqlite.db:/app/sqlite.db \  # Optional: for SQLite database
  -e MCP_DEBUG=1 \  # Optional: Enable debug mode
  mcp/dbutils --config /app/config.yaml

Add to Claude configuration:

"mcpServers": {
  "mcp-dbutils": {
    "command": "docker",
    "args": [
      "run",
      "-i",
      "--rm",
      "-v",
      "/path/to/config.yaml:/app/config.yaml",
      "-v",
      "/path/to/sqlite.db:/app/sqlite.db",  // Optional: for SQLite database
      "mcp/dbutils",
      "--config",
      "/app/config.yaml"
    ],
    "env": {
      "MCP_DEBUG": "1"  // Optional: Enable debug mode
    }
  }
}

Note for Docker database connections:

• For SQLite: Mount your database file using -v /path/to/sqlite.db:/app/sqlite.db
• For PostgreSQL running on host:
  • On Mac/Windows: Use host.docker.internal as host in config
  • On Linux: Use 172.17.0.1 (docker0 IP) or run with --network="host"

Requirements

• Python 3.10+
• PostgreSQL (optional)
• SQLite3 (optional)

Configuration File

The project requires a YAML configuration file, specified via the --config parameter. Configuration example:

databases:
  # Standard PostgreSQL configuration example
  my_postgres:
    type: postgres
    dbname: test_db
    user: postgres
    password: secret
    host: host.docker.internal  # For Mac/Windows
    # host: 172.17.0.1         # For Linux (docker0 IP)
    port: 5432

  # PostgreSQL with JDBC URL example
  my_postgres_jdbc:
    type: postgres
    jdbc_url: jdbc:postgresql://host.docker.internal:5432/test_db
    user: postgres            # Credentials must be provided separately

            
        
            
                    password: secret          # Not included in JDBC URL for security

  # SQLite standard configuration
  my_sqlite:
    type: sqlite
    path: /app/sqlite.db       # Database file path
    password: optional_password # optional

  # SQLite with JDBC URL configuration
  my_sqlite_jdbc:
    type: sqlite
    jdbc_url: jdbc:sqlite:/app/data.db?mode=ro&cache=shared  # Supports query parameters
    password: optional_password    # Provided separately for security

The configuration supports JDBC URL format for both PostgreSQL and SQLite:

PostgreSQL:

1. Standard configuration with individual parameters
2. JDBC URL configuration with separate credentials

SQLite:

1. Standard configuration with path parameter
2. JDBC URL configuration with query parameters support:
   • mode=ro: Read-only mode
   • cache=shared: Shared cache mode
   • Other SQLite URI parameters

Debug Mode

Set environment variable MCP_DEBUG=1 to enable debug mode for detailed logging output.

Architecture Design

Core Concept: Abstraction Layer

graph TD
  Client[Client] --> DatabaseServer[Database Server]
  subgraph MCP Server
    DatabaseServer
    DatabaseHandler[Database Handler]
    PostgresHandler[PostgreSQL Handler]
    SQLiteHandler[SQLite Handler]
    DatabaseServer --> DatabaseHandler
    DatabaseHandler --> PostgresHandler
    DatabaseHandler --> SQLiteHandler
  end
  PostgresHandler --> PostgreSQL[(PostgreSQL)]
  SQLiteHandler --> SQLite[(SQLite)]

The abstraction layer design is the core architectural concept in MCP Database Utilities. Just like a universal remote control that works with different devices, users only need to know the basic operations without understanding the underlying complexities.

1. Simplified User Interaction

• Users only need to know the database configuration name (e.g., "my_postgres")

• No need to deal with connection parameters and implementation details

• MCP server automatically handles database connections and queries

            #### 2. Unified Interface Design
  

• DatabaseHandler abstract class defines unified operation interfaces

• All specific database implementations (PostgreSQL/SQLite) follow the same interface

• Users interact with different databases in the same way

3. Configuration and Implementation Separation

• Complex database configuration parameters are encapsulated in configuration files
• Runtime access through simple database names
• Easy management and modification of database configurations without affecting business code

System Components

1. DatabaseServer

   • Core component of the MCP server
   • Handles resource and tool requests
   • Manages database connection lifecycle

2. DatabaseHandler

   • Abstract base class defining unified interface
   • Includes get_tables(), get_schema(), execute_query(), etc.
   • Implemented by PostgreSQL and SQLite handlers

3. Configuration System

   • YAML-based configuration file
   • Support for multiple database configurations
   • Type-safe configuration validation

4. Error Handling and Logging

   • Unified error handling mechanism
   • Detailed logging output
   • Sensitive information masking

Usage Examples

Basic Query

# Access through database name
async with server.get_handler("my_postgres") as handler:
    # Execute SQL query
    result = await handler.execute_query("SELECT * FROM users")

View Table Structure

# Get all tables
tables = await handler.get_tables()

# Get specific table schema
schema = await handler.get_schema("users")

Error Handling

try:
    async with server.get_handler("my_db") as handler:
        result = await handler.execute_query("SELECT * FROM users")
except ValueError as e:
    print(f"Configuration error: {e}")
except Exception as e:
    print(f"Query error: {e}")

Security Notes

• Supports SELECT queries only to protect database security

• Automatically masks sensitive information (like passwords) in logs

            - Executes queries in read-only transactions
  

API Documentation

DatabaseServer

Core server class providing:

• Resource list retrieval
• Tool call handling (list_tables, query)
• Database handler management

MCP Tools

list_tables

Lists all tables in the specified database.

• Parameters:
  • database: Database configuration name
• Returns: Text content with a list of table names

query

Executes a SQL query on the specified database.

• Parameters:
  • database: Database configuration name
  • sql: SQL query to execute (SELECT only)
• Returns: Query results in a formatted text

DatabaseHandler

Abstract base class defining interfaces:

• get_tables(): Get table resource list
• get_schema(): Get table structure
• execute_query(): Execute SQL query
• cleanup(): Resource cleanup

PostgreSQL Implementation

Provides PostgreSQL-specific features:

• Remote connection support
• Table description information
• Constraint queries

SQLite Implementation

Provides SQLite-specific features:

• File path handling
• URI scheme support
• Password protection support (optional)

Contributing

Contributions are welcome! Here's how you can help:

1. 🐛 Report bugs: Open an issue describing the bug and how to reproduce it
2. 💡 Suggest features: Open an issue to propose new features
3. 🛠️ Submit PRs: Fork the repo and create a pull request with your changes

Development Setup

1. Clone the repository
2. Create a virtual environment using uv venv
3. Install dependencies with uv sync --all-extras
4. Run tests with pytest

For detailed guidelines, see CONTRIBUTING.md

Acknowledgments

• MCP Servers for inspiration and demonstration
• AI Editors:
  • Claude Desktop
  • 5ire
  • Cline
• Model Context Protocol for comprehensive interfaces

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