AI agents are getting database access before they understand databases.
That is the wrong order.
A real production database is rarely self-explanatory. The important table is not always named orders. The customer table may be called t_bd_customer. A field may carry a business-critical status code that only makes sense if you know the system behind it. A warehouse may split raw operational data, cleaned dimensions, and aggregated facts across schemas with names like ods, dw, and staging. The schema is technically visible, but the meaning is not.
So I built db-semantic-mcp: a small MCP server that gives AI coding agents a safe semantic map of a database.
It exposes table names, column types, comments, sample rows, and LLM-powered schema search. It supports PostgreSQL and SQL Server. It works with MCP-compatible agent clients such as OpenCode, Claude Code, Cursor, and similar tools.
