Multi-Database MCP Server

Overview

The Multi‑Database MCP Server from Legion AI is a purpose‑built bridge that lets AI assistants—such as Claude or other MCP‑compatible agents—interact with a wide range of relational databases through a single, uniform interface. By leveraging the Legion Query Runner library and the MCP Python SDK, the server translates natural‑language or structured AI commands into concrete SQL queries, executes them across PostgreSQL, MySQL, SQL Server, BigQuery, and more, and returns the results in a format that can be consumed by downstream AI logic. This eliminates the need for developers to write custom connectors or learn disparate database client APIs, dramatically reducing integration friction.

What sets this MCP server apart is its zero‑configuration schema discovery and AI‑ready tool exposure. When a database is registered, the server automatically introspects its tables, columns, and relationships, publishing that metadata as MCP resources. These resources can then be queried by the AI agent to understand table structures, infer appropriate joins, or validate user intent. Additionally, a suite of pre‑built tools—such as “list tables,” “describe schema,” and “run arbitrary query”—are exposed through MCP, allowing agents to perform complex data operations without hard‑coding SQL logic. Developers can also extend the toolset or add custom prompts, giving the server flexibility to adapt to specialized business rules.

The server’s multi‑database capability means a single instance can manage connections to dozens of heterogeneous systems simultaneously. Users select the target database at runtime via a simple prompt or command, and the server routes queries to the correct backend. Credential handling is isolated from application code; authentication details are stored securely within the server’s configuration, ensuring sensitive information never leaks to client applications. This design is especially valuable in regulated environments where strict separation of duties and auditability are required.

Integration with modern AI workflows is straightforward. The server can run as a standalone MCP service or be embedded in FastAPI, LangChain, or other frameworks. AI agents communicate with it over the MCP protocol, sending context‑rich messages that include schema references or desired outputs. The server’s stateful interaction model allows agents to maintain session context across multiple queries, enabling multi‑step data exploration or iterative refinement of results. For example, a data analyst bot could ask for the latest sales figures, receive a summarized table, then request a deeper drill‑down on specific regions—all within the same conversational flow.

In summary, the Multi‑Database MCP Server delivers a unified, AI‑centric data access layer that abstracts away database heterogeneity, automates schema discovery, and exposes rich tooling for conversational agents. Its secure credential management, extensible design, and seamless integration with MCP‑compatible assistants make it an indispensable component for developers building data‑driven AI applications that require reliable, scalable access to diverse relational databases.