"Information about SQL (Structured Query Language)" matching MCP servers:

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables semantic search and retrieval over local Markdown/MDX documentation using Node.js-based embeddings. Supports multi-language documentation with offline vector indexing and MCP tool exposure for AI assistants.

Enables semantic search and interaction with Fuel Network documentation and Sway Language resources within AI-powered development environments. It provides high-quality technical context using a local vector database to assist with smart contract development and FuelVM queries.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

Integrates Redshift database query capabilities with vector-based knowledgebase tools for semantic search and RAG applications. It enables users to execute SQL queries, explore database schemas, and perform hybrid semantic searches on markdown files stored in S3.

Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

A production-grade Model Context Protocol server for PostgreSQL. Lets AI agents safely inspect, query, operate, and tune a Postgres database — over 100 tools spanning catalog introspection, query intelligence, natural-language SQL, structural diffs, hybrid search, graph queries, data movement, live ops, and more.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.

A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.

Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.

Enables GitHub Copilot to query local ChromaDB instances to retrieve relevant documents and context for AI conversations. It allows users to search vector collections using natural language tools directly within VS Code.

Enables AI-powered analysis of Ethereum blockchain data through semantic search, natural language queries, and structured filtering. Provides comprehensive access to addresses, transactions, blocks, tokens, and smart contracts with real-time blockchain intelligence.

Enables storing and retrieving information using vector embeddings with semantic search capabilities. Integrates with the AI Embeddings API to automatically generate embeddings for content and perform similarity-based searches through natural language queries.

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.

Allows AI models to query and interact with FalkorDB graph databases through the Model Context Protocol (MCP) specification.

An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.