Ultimate Database Query MCP Bridge

Ultimate Database Query MCP Bridge is an MCP server that provides AI assistants with a universal database query interface supporting multiple database engines including PostgreSQL, MySQL, SQLite, SQL Server, and other SQL-compatible databases. This MCP bridge enables language models to execute queries, explore schemas, analyze data, and generate reports across different database systems through a single unified connection, eliminating the need for separate MCP servers per database type.

When to Use This MCP Server

Connect this server when...

• You work with multiple database engines and want a single MCP server that connects to PostgreSQL, MySQL, SQLite, and others
• You need AI-assisted SQL query generation, optimization, and execution across different database platforms
• Your workflow involves exploring database schemas, understanding table relationships, and analyzing data through natural language
• You want a unified interface for querying development, staging, and production databases of different types
• You are migrating data between database systems and need AI help translating queries between SQL dialects

Consider alternatives when...

• You only use MongoDB or another NoSQL database that has its own dedicated MCP server
• You need real-time database monitoring and alerting rather than query execution
• You require database administration operations like user management, backup, or replication configuration

Quick Start

Copy
# .mcp.json configuration
{
  "mcpServers": {
    "database": {
      "command": "npx",
      "args": ["-y", "@mcp/database-query-bridge"],
      "env": {
        "DATABASE_URL": "postgresql://user:pass@localhost:5432/mydb",
        "READ_ONLY": "true"
      }
    }
  }
}

Connection setup:

1. Ensure Node.js 18+ is installed on your system
2. Obtain the connection string for your target database
3. Add the configuration above to your .mcp.json with your database connection details
4. Restart your MCP client to establish the database connection

Example tool usage:

# Explore schema
> Show me all tables in the database and their column definitions

# Query data
> Find the top 10 customers by total order value in the last quarter

# Analyze relationships
> What foreign key relationships exist between the orders and products tables?

Core Concepts

Concept	Purpose	Details
Connection Strings	Database targeting	Standard URI format for connecting to different database engines with credentials and options
Schema Discovery	Structure exploration	Automatic detection of tables, columns, indexes, foreign keys, and constraints across database types
Query Execution	Data retrieval	SQL query execution with result formatting, pagination, and timeout management
SQL Dialect Translation	Cross-database support	Handling differences in SQL syntax across PostgreSQL, MySQL, SQLite, and SQL Server
Read-Only Mode	Safety control	Restricting the server to SELECT queries only, preventing any data modification operations

Architecture:

+------------------+       +------------------+       +------------------+
|  Database        |       |  DB Query MCP    |       |  AI Assistant    |
|  PostgreSQL/     |<----->|  Bridge (npx)    |<----->|  (Claude, etc.)  |
|  MySQL/SQLite/   | driver|  stdio transport  | stdio |                  |
|  SQL Server      |       +------------------+       +------------------+
+------------------+
        |
        v
+------------------------------------------------------+
|  Schema > Tables > Columns > Queries > Results        |
+------------------------------------------------------+

Configuration

Parameter	Type	Default	Description
DATABASE_URL	string	(required)	Connection string in URI format specifying the database engine, host, credentials, and database name
READ_ONLY	boolean	false	Restrict server to read-only operations (SELECT, DESCRIBE) preventing any data modification
query_timeout	integer	30000	Maximum query execution time in milliseconds before automatic cancellation
max_rows	integer	1000	Maximum number of rows returned per query to prevent overwhelming large result sets
ssl_mode	string	prefer	SSL connection mode for encrypted database connections (disable, prefer, require, verify-full)

Best Practices

1. Always enable read-only mode for production databases. Set READ_ONLY=true to ensure the AI assistant cannot execute INSERT, UPDATE, DELETE, or DDL statements against production data. This is the most important safety measure for any production database connection.

2. Use schema discovery before writing queries. Before asking the AI to query data, have it explore the database schema first. Understanding table names, column types, and relationships ensures the AI generates accurate SQL that matches your actual data model.

3. Set query timeouts to protect database performance. Long-running queries can degrade database performance for all users. Configure query_timeout to automatically cancel queries that exceed a reasonable duration. For complex analytical queries, use higher timeouts but monitor execution impact.

4. Limit result set sizes for exploratory queries. The max_rows parameter prevents the AI from accidentally retrieving millions of rows. Start with limited result sets and increase only when you need comprehensive data. Use COUNT queries first to understand data volume before fetching rows.

5. Use SSL for remote database connections. When connecting to databases over a network, enable SSL mode to encrypt data in transit. Set ssl_mode to "require" or "verify-full" for production connections to prevent credential and data interception.

Common Issues

"Connection refused" when connecting to the database. Verify the database host, port, and credentials in your connection string. Check that the database server is running, your IP is whitelisted, and the specified port is accessible. For cloud databases, check security group and firewall rules.

SQL syntax errors across different database engines. SQL dialects vary between PostgreSQL, MySQL, and SQLite. The AI may generate syntax valid for one engine but not another. Specify which database engine you are using so the AI generates dialect-appropriate SQL.

Query returns "permission denied" for certain tables. The database user in your connection string may not have SELECT permission on all tables. Grant appropriate read permissions to the user or use a different user with broader access. Check the database's permission grants for the connected user.