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quarkiverse/mcp-server-jdbc

by Various

Model Context Protocol Servers in Quarkus

Q

MCP

quarkiverse/mcp-server-jdbc

Added 1 June 2026

#mcp #quarkus-app

Overview

Provides a Model Context Protocol (MCP) server for JDBC databases, built on Quarkus. It allows LLMs to query and interact with relational databases through a standard JDBC interface, enabling structured data access for AI tools.

Best for

Best for
Developers building AI tools that require direct, secure access to relational databases.

Use cases

  • Expose a relational database schema to an MCP-compatible AI agent.
  • Enable natural language querying of SQL databases via an LLM.
  • Integrate database access into MCP-based development workflows.

How to use

Install

jbang jdbc@quarkiverse/quarkus-mcp-servers

Tools exposed

  • jbang

Tested with

Claude Desktop

Notes

Provides a Model Context Protocol (MCP) server for JDBC databases, built on Quarkus. It allows LLMs to query and interact with relational databases through a standard JDBC interface, enabling structured data access for AI tools.

193 stars on GitHub. Last updated 2026-04-07. Licensed Apache-2.0.

Use cases

  • Expose a relational database schema to an MCP-compatible AI agent.
  • Enable natural language querying of SQL databases via an LLM.
  • Integrate database access into MCP-based development workflows.

Pros

  • Leverages Quarkus for fast startup and low memory footprint.
  • Standard JDBC support means compatibility with many databases.
  • Part of the quarkiverse-mcp-servers ecosystem for easy deployment.

Cons

  • Requires a Java runtime environment.
  • Limited to JDBC-compatible databases (no NoSQL or cloud-specific APIs).
  • May need manual configuration for complex or large schemas.

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Leverages Quarkus for fast startup and low memory footprint.
  • Standard JDBC support means compatibility with many databases.
  • Part of the quarkiverse-mcp-servers ecosystem for easy deployment.

Cons

  • Requires a Java runtime environment.
  • Limited to JDBC-compatible databases (no NoSQL or cloud-specific APIs).
  • May need manual configuration for complex or large schemas.
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