"Techniques for Analyzing and Interacting with Large MS SQL Databases" matching MCP servers:

Enables LLMs and agents to interact with relational databases (SQL Server, MySQL, PostgreSQL) through MCP tools. Supports executing queries, inserting records, listing tables, and exposing database schemas with secure credential management.

A governed SQL gateway that exposes typed tools to AI agents, compiling safe read-only queries from a semantic layer while blocking PII before execution, supporting SQL Server, Postgres, and SQLite.

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.

Enables natural language and SQL querying across SQLite, MySQL, PostgreSQL, and MongoDB databases with support for bulk operations and schema inspection. It features a safety mechanism requiring explicit user confirmation for destructive actions like data modification and deletion.

MCP server for managing and retrieving reusable SQL templates with semantic search, enabling a workflow of generation, retrieval, and templating.

Provides a read-only PostgreSQL SQL surface for LLM agents via MCP, with defense-in-depth security layers for safe database queries.

Cross-platform MCP server for SQL Server that enables AI assistants to explore schemas, relationships, and run read-only queries via natural language.

Provides LLM access to Exasol database via MCP tools, including metadata reading and data queries, with an optional on-premise text-to-SQL (Governed SQL) translation.

Enables querying a PostgreSQL database using natural language by retrieving context from Azure AI Search and generating SQL with Azure OpenAI, with validation and optional execution.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Enables AI assistants to interact with SQL Server Data Warehouses using natural language for automatic schema discovery and report generation. It ensures security by restricting operations to read-only SELECT queries through both code validation and database permissions.

Connects Microsoft Copilot Studio to Azure SQL Databases, enabling natural language interactions for data querying, record management, and schema inspection. It features 12 specialized tools for performing CRUD operations, executing SQL queries, and generating data visualizations like charts.

A FastMCP server that provides natural language interaction with MS SQL databases, enabling users to query data, list tables, describe structures, and execute database operations through a conversational AI interface.

MCP server enabling natural-language querying of SQLite databases via schema discovery, GraphRAG retrieval, and safely guarded read-only SQL execution.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A proof of concept MCP server for Apache OFBiz

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.