CData Microsoft SQL Server MCP Server

The Microsoft SQL Server MCP Server by CData is a lightweight, read‑only bridge that exposes Microsoft SQL Server databases to large language models through the Model Context Protocol (MCP). By wrapping CData’s JDBC driver, the server translates natural‑language queries from an AI assistant into SQL, retrieves live data, and returns results in a format that the model can consume without any direct SQL knowledge from the user. This solves the common problem of integrating structured database information into conversational AI workflows while keeping the data layer isolated and secure.

For developers, the server is valuable because it removes the need to build custom connectors or write boilerplate code for each database. Instead, a single MCP server instance can expose an entire SQL Server schema—or a curated subset of tables—through a standardized interface. The MCP protocol handles authentication, query parsing, and result serialization, allowing AI assistants like Claude Desktop to ask questions such as “Show me the latest sales figures for product X” and receive a concise, formatted answer drawn from the live database.

Key features include:

• Read‑only access to all tables or a specified list, ensuring that data integrity is maintained while still providing up‑to‑date insights.
• Automatic JDBC driver integration; the server loads the CData JDBC JAR and uses its connection string utility to validate connectivity.
• Property‑based configuration ( files) that lets developers define server metadata, driver paths, and table visibility without modifying code.
• MCP tool exposure with a customizable prefix, enabling the AI assistant to invoke database operations as simple tools (e.g., ).

Typical use cases span business intelligence, operational dashboards, and compliance reporting. For instance, a sales manager can query the assistant for “What was last quarter’s revenue by region?” and receive an instant, accurate response derived from the live SQL Server. In a DevOps context, engineers might ask for “Show recent error logs from the production database” and get a filtered list without writing any SQL.

Integration into AI workflows is straightforward: once the MCP server is running, developers add its entry to the assistant’s configuration file. The AI client then automatically discovers the server, lists available tools, and can invoke them as part of a conversation or a chain of reasoning. This seamless integration makes it possible to embed live data queries into generative tasks, analytics pipelines, or chatbot interactions without exposing database credentials or requiring developers to manage complex middleware.