mcp-sqlserver
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- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to securely interact with Microsoft SQL Server databases to query data, inspect schemas, and retrieve metadata with read-only operations by default and optional write capabilities.1MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to interact with Microsoft SQL Server databases through a standardized interface. Supports executing SQL queries, browsing database schemas, and viewing table data with flexible authentication options for both local and Azure SQL databases.6MIT
- AlicenseAqualityCmaintenanceEnables AI assistants to interact with Microsoft SQL Server databases through query execution, schema discovery, CRUD operations, stored procedures, and data export with built-in safety controls.18Apache 2.0
- FlicenseCqualityCmaintenanceEnables AI assistants to analyze and query SQL Server databases, including schema discovery, health checks, and data retrieval.10-
- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to connect to Microsoft SQL Server via the MCP protocol, supporting database schema queries, data reading, and arbitrary SQL execution.-
- AlicenseAqualityDmaintenanceEnables AI agents to securely connect to and query Microsoft SQL Server databases with read-only access, schema discovery, and relationship mapping. Features advanced security protections, health monitoring, and bulk operations for production environments.954 npmMIT
TDQS
Scored across 35 tools
Most tools target a distinct resource/action (e.g., list_tables vs describe_table vs get_indexes), and performance monitoring tools are separated by clear DMV-based descriptions. However, execute_query, export_query, and sample_table all execute or return query results, so an agent could sometimes confuse which one to use.
All tool names follow a consistent verb_noun snake_case pattern, such as list_tables, get_indexes, describe_procedure, and compare_schemas. There is no mixing of naming conventions or vague generic verbs.
With 35 tools, the surface is well beyond the typical well-scoped range and falls into the 25+ too-many category. While tools cover distinct areas, several could be consolidated or grouped into parameterized tools to reduce cognitive load.
The toolkit provides broad read-only coverage of SQL Server metadata, querying, schema generation, and performance monitoring, with no major dead ends for that scope. Missing write/DDL tools and user/permission introspection are notable but appear outside this server's read-only intent.