# Querying > Browse 13 items tagged with Querying. ## Items - [Oracle Autonomous AI Database MCP Server](https://mcpserver.ever.works/pl/items/oracle-autonomous-ai-database-mcp-server) — Oracle Autonomous AI Database MCP Server is a built-in, multi-tenant MCP server in Oracle's Autonomous AI Database Serverless (19c/26ai), exposing endpoints for AI agents to invoke Select AI Agent tools. Key features include native integration, enterprise security (RBAC, encryption, VPD), and OCI console management without standalone infrastructure. Ideal for governed AI data access in enterprise environments; surpasses open-source PostgreSQL MCP with deep Oracle security and multi-tenancy. - [Aurora DSQL MCP Server](https://mcpserver.ever.works/pl/items/aurora-dsql-mcp-server) — Aurora DSQL MCP Server provides AI agents with direct SQL query access to Amazon Aurora databases via MCP. Key features include open-source implementation by AWS Labs, easy installation via uvx, and support for direct SQL execution. Use cases encompass AI-driven data retrieval and analysis; compared to general PostgreSQL MCP, it offers native AWS Aurora optimization and serverless compatibility. - [DBHub Database](https://mcpserver.ever.works/pl/items/dbhub-database) — DBHub Database is a universal MCP server connecting AI assistants to multiple databases including MySQL, PostgreSQL, and SQLite for querying and management. It supports broad database compatibility and direct query execution. Perfect for AI agent data access across heterogeneous databases; stands out from single-DB MCP servers like PostgreSQL MCP by offering multi-DB support in one server. - [Elastic MCP](https://mcpserver.ever.works/pl/items/elastic-mcp) — Elastic MCP Server allows AI agents to interact with Elasticsearch databases through the Model Context Protocol. Key features include search and analytics query support tailored for Elastic. Suited for AI agent data retrieval in search-heavy use cases; differs from relational PostgreSQL MCP by focusing on full-text search and log analytics. - [MCP Neo4j Server](https://mcpserver.ever.works/pl/items/mcp-neo4j-server) — MCP Neo4j Server integrates AI agents with Neo4j graph databases via the Model Context Protocol, enabling Cypher queries and graph traversals. Key features include direct database interaction tools for LLMs. Suited for AI-driven graph data analysis and relationship mapping; contrasts with PostgreSQL MCP by focusing on graph structures rather than tabular data. - [NihFix.Postgres.Mcp](https://mcpserver.ever.works/pl/items/nihfixpostgresmcp) — NihFix.Postgres.Mcp enables real-time interactions between AI agents and PostgreSQL databases using SSE and STDIO protocols via MCP. Key features include live query execution and streaming responses. Use cases involve dynamic AI agent data access; similar to other PostgreSQL MCP servers but emphasizes real-time SSE connectivity. - [Prisma MCP Server](https://mcpserver.ever.works/pl/items/prisma-mcp-server) — Prisma MCP Server facilitates AI interactions with Prisma for database schema management, ORM operations, Postgres handling, and schema migrations. Key features include Prisma Console integration and official support. Use cases span AI-assisted database development; differs from raw PostgreSQL MCP by adding ORM layer for safer, typed queries. - [SchemaCrawler](https://mcpserver.ever.works/pl/items/schemacrawler) — SchemaCrawler MCP Server connects AI agents to relational databases for schema exploration and valid SQL generation via the Model Context Protocol. Key features include querying database structures and interpreting column meanings without direct access. Ideal for AI-assisted database analysis and documentation; complements PostgreSQL MCP by focusing on metadata over data querying. - [SchemaFlow](https://mcpserver.ever.works/pl/items/schemaflow) — SchemaFlow MCP Server delivers real-time PostgreSQL and Supabase database schema access to AI-IDEs through secure SSE connections via MCP. Key features include tools like get_schema, analyze_database, and check_schema_alignment for live context. Suited for AI agent development workflows; extends PostgreSQL MCP with real-time schema monitoring and Supabase integration. - [SQLite MCP Server](https://mcpserver.ever.works/pl/items/sqlite-mcp-server) — SQLite MCP Server offers safe, read-only access to SQLite databases for LLMs via the Model Context Protocol, using FastMCP with query validation. Key features emphasize security and exploration without write risks. Perfect for lightweight AI agent data retrieval; lighter alternative to PostgreSQL MCP for embedded, file-based databases. - [Supabase Query MCP](https://mcpserver.ever.works/pl/items/supabase-query-mcp) — Supabase Query MCP enables comprehensive management of Supabase databases via chat interfaces, supporting read/write queries, management APIs, migrations, and logs. Key features include end-to-end operations for AI agents. Use cases cover full-stack Postgres management; builds on PostgreSQL MCP with Supabase-specific APIs and versioning. - [Valkey MCP Server](https://mcpserver.ever.works/pl/items/valkey-mcp-server) — Valkey MCP Server provides MCP access to Valkey, a Redis-compatible in-memory key-value store, developed by AWS Labs for key operations and caching. Key features include uvx installation and high-performance queries. Ideal for AI agent caching and real-time data; contrasts PostgreSQL MCP with in-memory speed over persistent storage. - [Cnosuke MCP MySQL](https://mcpserver.ever.works/pl/items/cnosuke-mcp-mysql) — Facilitates MySQL database operations through a Go-based MCP server, enabling seamless integration with MCP clients for database querying and management. --- _Canonical page: https://mcpserver.ever.works/pl/tags/querying_