Database Explorer MCP: AI-to-Database Integration

Exploring a database can mean repeatedly switching between schema inspection, SQL, results and documentation. I built Database Explorer MCP to connect those tasks to the AI assistant a developer or analyst already uses.

My role: TypeScript MCP server development and database integration. The server connects an MCP-compatible client to PostgreSQL, MySQL, SQLite and MongoDB, exposing database exploration and query tools through a common interface.

The public project documents schema exploration, query execution, table statistics, query plans, CSV/JSON export and Mermaid ER diagrams. It also provides multi-connection configuration and client setup guides. The architecture is AI client → MCP server → database.

This demonstrates a practical integration pattern for AI-assisted data work: connect an existing interface to a defined business system and return inspectable results. For a client implementation, I would first scope the required tools, permitted data access and acceptance checks.

Independent public build. The linked repository contains the source, examples and setup instructions. It is an MCP server used through a compatible client. Database credentials, access controls and deployment are configured for each environment.

Need AI connected to a database or business tool? Send the system, the task users need to complete and the access boundaries. I can start with one connector, a small set of useful tools and an agreed test workflow.