"MongoDB" matching MCP servers:

Enables interaction with MongoDB databases through MCP, supporting database queries, resource reading, and multi-collection operations including aggregation pipelines and lookups.

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.

MCP server for Hydra ETL: lets AI assistants write declarative YAML ETL pipelines (CSV, Parquet, PostgreSQL, MySQL, MongoDB, APIs) and validate them before any data is read or written.

Enables querying and mutating data across multiple sources (CSV, Google Sheets, SQLite, PostgreSQL, MySQL, MongoDB, Oracle) through a dynamic GraphQL API, with flexible filtering, batching, persisted queries, and dynamic source switching.

Celp-MCP enables natural-language analytics over SQL, MongoDB, and Databricks warehouses through MCP-compatible clients, converting questions into multi-step database plans and returning markdown reports.

Provides an instant MongoDB MCP server that can be used from any AI agent without local installation.

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 interaction with the Unstructured API to manage data workflows, including creating and managing source connectors (S3, Azure, Google Drive, etc.), destination connectors (Weaviate, Pinecone, MongoDB, etc.), and workflows for document processing and data pipelines.

A universal database adapter that enables read-only queries across PostgreSQL, Supabase, and MongoDB databases via the Model Context Protocol.