Universal Database MCP Server

The Universal Database MCP Server bridges large language models with relational data stores by exposing a lightweight, read‑only interface over the Model Context Protocol. It solves the common pain point of giving AI assistants instant, secure access to database schemas and data without risking accidental writes or leaking sensitive information. Developers can now ask a model, “Show me the columns in ” or “What does the primary key of look like?” and receive accurate, real‑time answers without writing custom adapters for each database.

At its core the server connects to any of the major relational engines—MySQL, PostgreSQL, Oracle, SQL Server, or SQLite—and offers two primary services: metadata discovery and read‑only query execution. Metadata endpoints expose table structures, column types, comments, primary keys, indexes, and foreign‑key relationships. The query endpoint allows arbitrary statements to be executed safely; all writes are blocked by design. Sensitive fields are automatically masked so that the model never sees personal data, ensuring compliance with privacy best practices.

Key capabilities include:

• Multi‑database support: a single server instance can target any supported engine with simple configuration.
• Schema introspection: the model receives structured JSON describing tables, columns, and constraints, enabling it to generate SQL templates or data dictionaries on the fly.
• Sample data previews: lightweight requests return a handful of rows per table, useful for quick sanity checks or documentation generation.
• SSE streaming: responses are sent via Server‑Sent Events, keeping the client connection lightweight and avoiding WebSocket overhead.
• MCP compliance: all communication follows the standard MCP message format, making integration with Claude or other LLMs straightforward.

Typical use cases span the full AI‑data lifecycle. In data engineering pipelines, a model can auto‑generate migration scripts or validate schema changes before they hit production. In business analytics, analysts can ask the model to describe a table’s purpose or fetch sample rows, accelerating report creation. For security reviews, the server’s automatic masking ensures that audit queries never expose PII while still allowing thorough inspection.

Integrating the server into an AI workflow is as simple as adding a configuration block to the LLM’s desktop config. Once registered, the assistant can invoke database tools via MCP commands, receive structured responses, and even compose SQL queries that respect the read‑only constraint. The server’s lightweight design and standard protocol make it a drop‑in component for any environment where developers need instant, safe access to relational data through conversational AI.