"How to connect to SQL Server" matching MCP servers:

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

An example server that enables interaction with Alibaba Cloud's Lindorm multi-model NoSQL database, allowing applications to perform vector searches, full-text searches, and SQL operations through a unified interface.

MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.

Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.

An MCP server that enables AI assistants to directly interact with Elasticsearch for searching, aggregating, and retrieving documents from indices, supporting full-text search, semantic search, and various query modes.

A TypeScript implementation of the Model Context Protocol server that enables creation, management, and semantic search of memory streams with Mem0 integration.

RAG document search MCP server that allows AI assistants to search a local document set and retrieve grounded passages via keyword (SQLite FTS5) or semantic (Chroma) backends.

The MCP Server for Weaviate facilitates integration with Weaviate using a customizable Python-based server, enabling interaction with Weaviate databases and OpenAI APIs via configurable URL and API keys.

Local-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.

Universal MCP server for readonly-first access to Oracle, SQL Server, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, and Qdrant vector search.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

A Machine Conversation Protocol server that enables AI agents to perform Retrieval-Augmented Generation by querying a FAISS vector database containing Sui Move language documents.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Uses Ollama or OpenAI to generate embeddings. Docker files included

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded

Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.

An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.

A server that enables Claude AI to interact with Weaviate vector databases, supporting both search and storage operations through Anthropic's MCP protocol.

An MCP server that enables Claude Desktop or Claude Code to ingest and search local documents using vector embeddings, returning relevant chunks as tool results for the model to reason over.