MCP Servers

Provides AI agents with access to ClickHouse analytical databases through the Model Context Protocol. Enables running analytical queries, exploring table schemas, inspecting materialized views, and monitoring query performance. Designed for OLAP workloads with support for large result sets and query profiling.

Official MCP server for interacting with MongoDB databases and MongoDB Atlas. Enables AI agents to query collections, run aggregations, manage indexes, inspect schemas, and perform CRUD operations. Also supports Atlas cloud management including cluster provisioning, database user management, performance advisor, and stream processing. Supports read-only mode for safe exploration.

MCP server for SQLite database interaction and business intelligence. Enables AI agents to query SQLite databases, create and modify tables, run analytical queries, and generate insights from data. Supports read-write operations with transaction safety and provides schema introspection for understanding database structure.

Official Supabase MCP server for database and backend integration. Enables AI agents to query PostgreSQL databases, manage tables and schemas, handle authentication users, interact with storage buckets, and invoke edge functions. Provides full access to Supabase project management including migrations and type generation.

MCP server for Neon's serverless PostgreSQL platform. Enables AI agents to manage Neon projects, branches, databases, and roles. Supports creating database branches for development and testing, running SQL queries, managing connection strings, and performing schema migrations. Leverages Neon's instant branching for safe experimentation without affecting production data.

MCP server for Airtable that lets AI agents read and write records, list bases and tables, inspect field schemas, and run filtered queries. Ideal for turning Airtable into a lightweight backend or knowledge base that agents can manage conversationally.

MCP server for MySQL and MariaDB that lets AI agents inspect schemas, list tables, and run parameterized SQL queries. Supports read-only or read-write modes so agents can explore data, debug, and prototype against a MySQL database with controlled access.

Provides read-only access to PostgreSQL databases through the Model Context Protocol. Enables AI agents to inspect database schemas, run SELECT queries, and explore table structures. Designed for safe database exploration with read-only transaction isolation to prevent accidental data modification. Originally part of the reference servers, now archived and available in servers-archived.

Official Notion MCP server for workspace integration. Enables AI agents to search pages, read content, create new pages, update existing pages, and manage databases in Notion. Supports rich text content, database queries with filters, and page property management for seamless knowledge base interaction from AI-powered tools.

Official Cloudflare MCP server for managing Cloudflare services. Enables AI agents to interact with Workers, KV namespaces, R2 storage, D1 databases, and DNS records. Supports deploying Workers scripts, managing environment variables, querying analytics, and configuring security settings across Cloudflare's edge network.

Connects AI agents to ChromaDB vector databases for semantic search and retrieval augmented generation (RAG) workflows. Supports creating collections, upserting documents with embeddings, querying by semantic similarity, and managing metadata filters. Ideal for knowledge base and document retrieval applications.

Official Snowflake MCP server enabling AI agents to query and analyze data in the Snowflake AI Data Cloud. Supports running SQL against warehouses, exploring databases and schemas, describing tables, and invoking Cortex AI services for search and analytics, with role-based access control honored end to end.

MCP server for the Weaviate open-source vector database. Enables AI agents to store objects, run semantic and hybrid searches, and manage collections, making it a memory and retrieval backend for RAG applications directly from AI-powered tools.

Official MCP server for the Milvus vector database. Lets AI agents create collections, insert vectors, and run similarity and scalar-filtered searches over large-scale embedding data, enabling retrieval and long-term memory for AI applications.

Pinecone's official MCP server connects AI assistants to Pinecone vector databases for retrieval-augmented generation (RAG) workflows. Lets agents create and configure indexes, upsert and embed documents, and run semantic searches over vector data — all from natural language, without leaving the editor or chat.