What is the MSSQL MCP Server?

It's an enterprise-grade Model Context Protocol (MCP) server that integrates advanced AI tooling directly with Microsoft SQL Server, offering 20 specialized tools for comprehensive database management and administration.

How does this tool benefit enterprise SQL Server users?

It provides AI-powered semantic schema discovery, detailed data profiling, and safe, audited data operations within multi-environment setups. This streamlines complex tasks and ensures compliance for DBAs and data engineers.

Is it compatible with AI assistants and clients?

Yes, being MCP-native, it works with any Model Context Protocol-compatible client, such as Windsurf or Claude Desktop, enabling AI assistants to interact effectively with your SQL Server instances.

What types of authentication does it support?

It supports SQL Server Authentication, Windows Authentication (NTLM), and Azure AD Authentication, allowing integration into various enterprise IT environments.

What security and governance features are included?

Key features include preview and explicit confirmation for all mutation (UPDATE, DELETE, DROP) operations, multi-environment support with granular policy controls, and comprehensive audit logging of every tool invocation.

SQL Server

Name: SQL Server
Author: ConnorBritain

•

Integrates AI tooling with Microsoft SQL Server through an enterprise-grade Model Context Protocol server, offering 20 tools for comprehensive schema discovery, data operations, and administration.

SQL Server

•

Integrates AI tooling with Microsoft SQL Server through an enterprise-grade Model Context Protocol server, offering 20 tools for comprehensive schema discovery, data operations, and administration.

关于

The SQL Server MCP Server is an enterprise-grade Model Context Protocol server designed for serious Microsoft SQL Server workflows. Moving beyond basic query generation, it empowers AI assistants to handle the complexities of real-world database management, including navigating massive, unfamiliar schemas, understanding intricate data shapes, and performing operations safely in regulated environments. With robust features for secure data operations, multi-environment support, and audit logging, it ensures AI tooling is genuinely useful and production-ready for enterprise SQL Server instances, enabling confident and rapid validation in UAT and deployment to production.

主要功能

Semantic schema discovery with wildcard and fuzzy matching, supporting pagination for large databases.
Comprehensive table profiling including column statistics (null %, cardinality, min/max/avg/median/p90) and optional row samples.
Multi-environment support with configurable database environments, authentication modes, and granular policy controls.
Audit logging of every tool invocation with timestamps, redacted arguments, and results for compliance and traceability.
2 GitHub stars
Preview and explicit confirmation for all mutation operations (UPDATE, DELETE, DROP) to prevent accidental data changes.

使用案例

Exploring and understanding complex or unfamiliar SQL Server schemas efficiently with AI assistance.
Profiling data shape and validating data pipelines in UAT environments before production deployment.
Automating secure data operations and administrative tasks for DBAs, data engineers, and ETL developers.

关于

主要功能

Semantic schema discovery with wildcard and fuzzy matching, supporting pagination for large databases.
Comprehensive table profiling including column statistics (null %, cardinality, min/max/avg/median/p90) and optional row samples.
Multi-environment support with configurable database environments, authentication modes, and granular policy controls.
Audit logging of every tool invocation with timestamps, redacted arguments, and results for compliance and traceability.
2 GitHub stars
Preview and explicit confirmation for all mutation operations (UPDATE, DELETE, DROP) to prevent accidental data changes.

使用案例

Exploring and understanding complex or unfamiliar SQL Server schemas efficiently with AI assistance.
Profiling data shape and validating data pipelines in UAT environments before production deployment.
Automating secure data operations and administrative tasks for DBAs, data engineers, and ETL developers.