"Understanding SQL Server Schema and Querying" matching MCP servers:

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

A read-only MCP server for SQL Server and PostgreSQL that enables exploring and querying databases (schemas, tables, views, procedures, indexes, foreign keys) and running arbitrary SELECT queries.

Exposes tools that let AI coding agents browse a repo's schema snapshot of tables and columns and validate their SQL against it before running or writing it, so queries referencing invented or stale table and column names get caught and corrected up front. Provides list_tables, describe_table, search_columns, and check_sql over the same snapshot used by the CLI, hook, and CI check.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

An extensible read-only MCP server for SQL databases, enabling schema exploration and safe SELECT queries via tools like list_schemas, list_tables, describe_table, and execute_query.

An MCP server that provides targeted PostgreSQL schema access to AI agents, retrieving only relevant tables for a question and enforcing read-only access through multiple layers.

Enables natural-language querying and analysis of an e-commerce SQLite database via MCP, with read-only SQL execution, table inspection, and AI-generated answers.

Provides an AI agent with read-only PostgreSQL access, enabling schema browsing, foreign-key exploration, column search across tables, capped SELECT queries, and offline data dictionary export while rejecting write operations.

Runs SQL statements in memory against real PostgreSQL 18 and SQLite 3.49 engines after loading your schema and sample rows, returning each statement's own verdict: errors with line, column, and hints, constraint violations, or the resulting rows. It also compares both dialects to expose portability differences, all with no database server, network, or API key.

An MCP server that connects LLMs to SQL databases for development assistance, enabling query execution, schema exploration, and data manipulation while providing safety controls against destructive operations.

An MCP server that connects AI assistants to Microsoft SQL Server databases, enabling schema exploration and read-only queries safely.

MCP server providing token-efficient access to OpenAPI/Swagger specs via MCP Resources for client-side exploration.

An MCP server that uses Google's Gemini API to analyze videos and convert them to text descriptions that Claude Code can understand and act upon.

A Model Context Protocol server that provides AI assistants with comprehensive access to SQL databases, enabling schema inspection, query execution, and database operations with enterprise-grade security.

Enables interaction with Microsoft SQL Server databases using both SQL Server and Windows Authentication. It supports flexible connection configurations, including read-only modes and encrypted communication for secure data management.

Enables AI models to interact with multiple database types (MySQL, MSSQL, Oracle, Sybase) through a unified interface with standardized tools for querying, inspecting schemas, and managing connections without requiring separate server configurations.

MCP server for querying H3Yun SQL data, enabling AI to execute read-only SQL queries and troubleshoot H3Yun business issues.

Gives an LLM read-only, schema-aware SQL database access so users can ask business questions in plain English and receive the exact result set, with malformed queries returned as errors for automatic retry and correction.

Enables read-only interaction with SQL databases through MCP, providing database metadata exploration, sample data retrieval, and secure query execution. Supports MySQL with multiple transport options and built-in security features including SQL injection protection and data sanitization.

A MCP server that exposes OpenAPI schema information to LLMs like Claude. This server allows an LLM to explore and understand large OpenAPI schemas through a set of specialized tools, without needing to load the whole schema into the context