Quarkus Model Context Protocol (MCP) Server

Quarkiverse Quarkus MCP Server

The Quarkiverse Quarkus MCP Server brings the Model Context Protocol (MCP) into the Quarkus ecosystem, giving developers a lightweight yet powerful way to expose their Java services as AI‑ready endpoints. MCP is designed to bridge large language models (LLMs) with external data sources and executable tools, enabling conversational agents to fetch files, run business logic, or retrieve prompts on demand. By implementing the MCP server as a Quarkus extension, this project leverages CDI, annotations, and Quarkus’s native compilation to deliver a fast, modular solution that can run as a native executable or in any container.

At its core, the server exposes three MCP concepts through declarative annotations on CDI beans:

• Tools – Methods annotated with become callable actions that an LLM can invoke. The framework handles serialization of arguments and results, so developers only need to write plain Java logic.
• Prompts – annotations define reusable prompt fragments that the model can retrieve and combine with user messages. This allows consistent, versioned prompting without hard‑coding strings in application code.
• Resources – annotated methods expose files or blobs (e.g., configuration, code snippets) to the LLM via URI. The server streams binary data efficiently using Server‑Sent Events (SSE), the only supported transport at present.

These capabilities are wired together automatically by Quarkus’s CDI container. A single bean can declare multiple tools, prompts, and resources, and the extension generates the required HTTP endpoints behind the scenes. The result is a self‑contained service that follows the MCP spec, making it compatible with any LLM client—Claude, OpenAI, or custom models—that understands the protocol.

Typical use cases include:

• Code assistance – Exposing a code‑generation or linting tool that an LLM can call to transform user input into runnable snippets.
• Data retrieval – Serving configuration files, logs, or domain models to the assistant so it can answer context‑aware questions.
• Prompt orchestration – Providing a library of prompt templates that can be composed dynamically, ensuring consistent tone and structure across interactions.

Because the server is built on Quarkus, it inherits the framework’s fast startup time and low memory footprint. Developers can compile the application to a native image with GraalVM, enabling deployment in serverless environments or edge devices. The declarative style also reduces boilerplate: a single annotation turns a method into an MCP endpoint, eliminating the need for manual routing or JSON handling.

In summary, the Quarkiverse Quarkus MCP Server gives Java developers a ready‑made, standards‑compliant bridge between their business logic and AI assistants. It streamlines tool integration, prompt management, and resource sharing while keeping performance high—making it an attractive choice for building intelligent, data‑aware applications on top of the Model Context Protocol.