Database

Snowflake documents a Snowflake-hosted Model Context Protocol (MCP) endpoint that fronts governed Snowflake data and Cortex workloads without provisioning a separate MCP bridge VM. Administrators declare tools with SQL (`CREATE MCP SERVER`)—for example Cortex Search queries, Cortex Analyst chat-style messages, Cortex Agent executions, parameterized SQL runners, or custom tools backed by Snowflake-native functions—and clients authenticate using Snowflake OAuth against the MCP revision pinned in Snowflake release notes.

The DuckDB-distributed community extension `duckdb_mcp` embeds MCP client and server capabilities directly inside DuckDB. Installers load it via `INSTALL duckdb_mcp FROM community` followed by `LOAD duckdb_mcp`, after which SQL can attach remote MCP servers (stdio/TCP/WebSocket transports), enumerate resources (`mcp_list_resources`), invoke remote tools (`mcp_call_tool`), and wrap responses with `read_csv`/`read_json`/`read_parquet` URIs routed through `mcp://`. In reverse direction, DuckDB can publish tables, queries, and execution-bound tools (`mcp_publish_table`, `mcp_publish_query`, `mcp_publish_execution_tool`) while `mcp_server_start` exposes them to external MCP-compatible clients.

Official Neon MCP integration exposes Neon Postgres projects to MCP-capable assistants via Streamable HTTP (`https://mcp.neon.tech/mcp`), legacy SSE (`https://mcp.neon.tech/sse`), or a locally launched `@neondatabase/mcp-server-neon` package. Documentation lists tools for project and branch lifecycle, SQL execution, migration rehearsal branches, slow-query diagnostics, Neon Auth provisioning, Data API setup, and embedded Neon docs retrieval—each mapped to Neon API operations.

Official Qdrant MCP server implementation that gives AI agents a semantic memory layer backed by Qdrant vector search. It exposes MCP tools for storing information and retrieving relevant context, so assistants can persist and recall facts across sessions instead of relying only on short chat history.

Provides AI agents access to Prisma schemas, migration planning, and database introspection capabilities. Agents can propose migrations, explore data models, and generate type-safe queries based on your Prisma setup. Works with local projects and remote Prisma Data Proxy deployments.

Allows AI agents to run queries, inspect collections, execute aggregation pipelines, and analyze explain plans against MongoDB clusters. Debug document models and performance issues by having the agent explore data structures directly. Supports MongoDB Atlas federated queries and Atlas Search.

Enables AI agents to inspect Pinecone vector database indexes, query vectors, manage collections, and debug retrieval behavior. Useful for teams using vector search in RAG applications who want to understand index statistics and optimize similarity search performance.

Exposes Redis key-value store operations to AI agents including GET/SET, list operations, hash manipulation, and pub/sub debugging. Agents can inspect caches, debug session stores, and check feature flags without dumping entire databases. Supports Redis Cluster and Sentinel configurations.

Connects agents to Supabase projects for table inspection, Edge Function debugging, database triggers, and real-time subscriptions. Agents can inspect RLS policies, view logs, and manage database types without the Supabase dashboard. Supports both hosted and self-hosted Supabase instances.

Enables AI agents to execute read-only SQL queries against PostgreSQL databases, inspect table schemas, and analyze query performance. Agents can debug data issues or prepare analytics without requiring direct database credentials in the conversation. Supports connection pooling and multiple database targets.

Provides lightweight SQL access to local SQLite database files for quick analytics, schema inspection, and prototyping without database server overhead. Agents can query, analyze, and generate reports from embedded datasets. Ideal for app sandboxes, development databases, and data exploration.

pg-mcp-server is a Model Context Protocol server implementation that connects AI agents to PostgreSQL databases. It registers database schemas as MCP resource templates and exposes SQL execution as an MCP tool. Agents can introspect table structures, run parameterized queries, and manage transactions without leaving the chat interface. Designed as a reference implementation for database MCP integrations.