SQL Server MCP for Claude

Overview

The MCP Server for SQL Server provides a seamless bridge between Claude and any Microsoft SQL Server instance. By exposing a local MCP endpoint, it lets an AI assistant translate natural‑language questions into real SQL queries, execute them against the database, and return structured results for further interpretation. This eliminates the need for manual query writing or custom connectors, enabling developers to ask complex analytical questions in plain English and receive immediate, data‑driven answers.

The server solves a common pain point: integrating AI assistants with on‑premise or cloud databases that traditionally require ODBC drivers, custom authentication layers, or intermediate APIs. It uses the lightweight pymssql library to establish a direct TCP connection, sidestepping ODBC and reducing deployment friction. The MCP implementation adheres to Claude’s “2025‑03‑26” protocol version, ensuring compatibility with the latest assistant features and JSON‑RPC communication standards. The architecture is intentionally minimal—only a single Python virtual environment, FreeTDS configuration on macOS, and straightforward command‑line scripts—making it easy to install, audit, and maintain.

Key capabilities include:

• Direct SQL execution via the tool, allowing any valid T‑SQL statement to be run.
• Schema discovery by querying , so Claude can dynamically learn about tables, columns, and data types before constructing queries.
• Cross‑platform support on macOS and Ubuntu Linux, with automatic FreeTDS installation on macOS.
• Secure operation by recommending read‑only credentials and providing clear guidelines to avoid accidental data modification.
• Protocol compliance with proper JSON‑RPC framing and headers, ensuring reliable message parsing by Claude.

In real‑world scenarios, this server is invaluable for data analysts who need rapid insights without writing code, developers building conversational dashboards that query live databases, or QA teams verifying data integrity through natural‑language queries. For example, a product manager can ask Claude to “show the top 10 customers by revenue” and receive an instantly generated report, while a backend engineer can embed the MCP endpoint into a larger microservice that delegates analytical workloads to Claude. The server’s lightweight footprint and strict security posture make it suitable for production environments where data governance is critical.

By integrating this MCP server into an AI workflow, teams can dramatically reduce the time from question to answer, lower the barrier for non‑technical stakeholders to access data, and leverage Claude’s language understanding to generate precise, context‑aware SQL. The result is a powerful, developer‑friendly tool that turns conversational AI into a direct, trusted query interface for SQL Server.