"mongodb" matching MCP servers:

A Model Context Protocol (MCP) server that enables LLMs to interact directly with MongoDB databases. Query collections, inspect schemas, and manage data seamlessly through natural language.

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 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.

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

A protocol server that enables LLMs like Claude to interact with MongoDB databases, providing tools for schema exploration, aggregation queries, and data analysis through natural language in Cursor.

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

A production-grade MCP server that provides persistent long-term memory for AI agents using MongoDB, enabling them to store, search, update, delete, retrieve, and summarize structured project memories across developer workflows.

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.

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 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.

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 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.

Enables AI-assisted chiller troubleshooting by exposing MCP tools to retrieve operational data, alarms, telemetry, and service history from MongoDB Atlas, plus vector and hybrid search over knowledge bases.

MCP server with 32 tools for ETL ingestion, AI-generated data quality rules, AI transformations, vector search, and natural-language SQL. Works across Postgres, MongoDB, Kafka, S3/MinIO, HashiCorp Vault, and five vector stores (Qdrant, Weaviate, Milvus, Chroma, pgvector).

Enables automatic, project-scoped long-term memory for LibreChat conversations by recalling relevant memories before each turn and writing durable facts after each turn. It integrates via a proxy that resolves project context from MongoDB and exposes MCP tools for explicit memory management.

A smart MCP gateway that routes AI agents to the right tools using hybrid search (vector + full-text) on MongoDB Atlas, collapsing the retrieval infrastructure into a single query.

Provides universal database operations for AI assistants through MCP, supporting 40+ databases including PostgreSQL, MySQL, MongoDB, Redis, and SQLite with built-in introspection tools for schema exploration.

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.