SQL Analyzer MCP Server

Overview

The mcp-server-sql-analyzer gives AI assistants a powerful, on‑demand SQL toolkit that validates syntax, converts between dialects, and dissects query structure. It is built on SQLGlot, a mature SQL parsing library, which means the server can understand a wide array of database flavors such as MySQL, PostgreSQL, SQLite, BigQuery, Snowflake, and more. By exposing these capabilities through MCP, Claude or any other MCP‑compatible assistant can ask the server to check a query before it is sent to a database, ensuring that users receive only syntactically correct and dialect‑appropriate SQL.

For developers, this server removes the need to embed a full SQL parser or maintain separate migration scripts. Instead of writing custom linting logic, developers can simply call the tool to surface errors or warnings. When migrating legacy reports or dashboards, the tool translates a query from one dialect to another while preserving semantics—an essential feature for teams that span multiple database platforms. The analysis tools ( and ) expose the underlying schema usage, allowing assistants to explain query logic, suggest optimizations, or verify that a user is referencing the correct tables and columns.

Typical use cases include:

• Query validation in interactive SQL editors or chatbot interfaces, preventing syntax errors from reaching the database.
• Cross‑dialect migration for data engineers moving workloads between cloud vendors or on‑premise systems.
• Schema-aware assistance where the assistant can point out missing joins, unused columns, or potential performance bottlenecks.
• Educational tooling that teaches SQL best practices by highlighting problematic patterns or recommending idiomatic syntax for a target dialect.

Integration is straightforward: an MCP client sends JSON requests to the server’s endpoints, and receives structured responses that can be rendered or acted upon by the assistant. Because the server is stateless and runs as a lightweight process, it scales horizontally with minimal overhead. Its unique advantage lies in the combination of validation, dialect conversion, and semantic analysis all wrapped in a single, well‑defined protocol—providing developers with a one‑stop solution for any SQL‑centric AI workflow.