"How to access my MongoDB database" matching MCP servers:

Enables chat-based interaction with PostgreSQL databases, supporting table listing, queries, write operations, and saved parameterized queries, deployable as a remote MCP server via Cloudflare Workers.

Full featured MCP Server for MongoDB database analysis.

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.

Enables LLM clients to query MongoDB collections read-only through a small set of guarded operations, exposing only declared fields and enforcing scoped filters, projections, and limits.

Enables AI agents to securely connect to MongoDB databases via MCP, with deployment modes from basic to RBAC-gated with Keycloak authentication.

Enables querying and analyzing the MongoDB sample_mflix movie dataset through natural language, supporting searches by title, genre, actors, directors, ratings, and providing aggregate statistics like counts and averages.

MCP server for multiple databases (PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis) with tools for schema inspection, querying, performance diagnostics, and safe write operations, featuring access modes, PII masking, and audit logging.

MCP server for querying and managing multiple databases (SQLite, PostgreSQL, MySQL) with read-only mode and schema inspection.

A template MCP server that provides a basic structure and configuration for building Model Context Protocol servers. Includes Docker support, environment configuration, and examples for both STDIO and SSE transport modes.

A Model Context Protocol server that enables AI models to interact with both MySQL and MongoDB databases through a standardized interface, supporting comprehensive database operations including queries, schema management, and CRUD operations.

Provides AI assistants with read-only access to inspect database schemas, preview data, and run safe queries across PostgreSQL, MySQL, MongoDB, and SQL Server. It enables AI tools to understand database structures and relationships automatically to generate more accurate code.

Enables interaction with MongoDB databases and MongoDB Atlas for querying data and managing clusters via the Model Context Protocol. It supports both local and cloud-based deployments using connection strings or Atlas API credentials.

An extensible MCP server for database operations that supports PostgreSQL for managing schemas, tables, data, and user permissions. It features automatic migration recording for DDL changes and integrates with various AI-powered editors like Cursor, Zed, and Claude Code.

A modular MCP server that enables interaction with multiple database types including PostgreSQL, MySQL, SQLite, Redis, MongoDB, and LDAP. It provides tools for executing queries, managing SQL commands, and exploring database schemas with configurable read-only security.

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

Provides a natural language interface for querying and managing PostgreSQL, MySQL, MariaDB, MSSQL, and SQLite databases using the Model Context Protocol. Users can explore database schemas and visualize query results through an integrated web dashboard.

Provides Claude with direct access to databases including SQLite, SQL Server, PostgreSQL, and MySQL, enabling execution of SQL queries and table management through natural language.

Provides database access capabilities to Claude with support for both SQLite and SQL Server databases. It enables users to manage schemas, execute SQL queries, and export results through natural language interaction.

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.

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.