"Information about MongoDB database" matching MCP servers:

Provides persistent memory for AI assistants using MongoDB Vector Search, enabling content storage, semantic search, and knowledge retrieval through MCP tools.

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

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.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.

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.

ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.

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

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 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.

Enables AI agents to securely query PostgreSQL with pgvector, DynamoDB, and MongoDB Atlas with Vector Search through a read-only, allowlisted MCP interface.

Enables interaction with KDB.AI through natural language for vector database operations, similarity searches, hybrid search, and advanced data analysis.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

A knowledge base MCP server backed by Qdrant vector database with local embeddings for semantic search and document management.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.

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

Enables interaction with DataStax Astra DB through the Model Context Protocol. Provides database connectivity and operations for Astra DB instances via secure token-based authentication.