"Information about MongoDB" matching MCP servers:

A Model Context Protocol server that provides read-only access to MongoDB databases, enabling AI assistants to directly query and analyze MongoDB data while maintaining data safety.

A Model Context Protocol server that enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.

Provides read-only access to MongoDB databases for LLMs to inspect collection schemas and execute aggregation pipelines.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

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.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Enables LLMs to create, query, and manage MongoDB-compatible databases using natural language without actual data storage. Uses statistical modeling to generate realistic data on-the-fly from sample documents or descriptions.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

A Model Context Protocol server that enables LLMs to interact with databases (currently MongoDB) through natural language, supporting operations like querying, inserting, deleting documents, and running aggregation pipelines.

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

Enables natural language querying of MongoDB data by transforming AI agent questions into MongoDB aggregations. Supports secure data separation, semantic vector search, and advanced lookup capabilities for database interactions.

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG 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.

A powerful Model Context Protocol (MCP) server implementation that provides standardized interaction with MongoDB databases, supporting complete CRUD operations, async patterns, and real-time updates via SSE.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.