Overview

The Quarkus MCP Server is a lightweight, extensible framework that turns any Quarkus application into an MCP (Model Context Protocol) server. By exposing prompts, resources, and tools through annotated CDI beans, developers can seamlessly bridge large‑language‑model (LLM) assistants with their own domain logic and data stores. This eliminates the need to hand‑craft HTTP or SSE endpoints, allowing teams to focus on business rules while still offering a fully compliant MCP interface.

Solving the integration bottleneck

LLM applications often require real‑time access to external APIs, file systems, or custom business services. Traditional integration patterns involve writing adapters for each tool, managing authentication, and handling streaming responses manually. The Quarkus MCP Server abstracts these concerns by providing declarative annotations (, , ) that automatically register methods as MCP endpoints. As a result, developers can expose complex functionality with minimal boilerplate while maintaining type safety and CDI lifecycle management.

Core capabilities in plain language

• Prompt endpoints: Methods annotated with generate conversational messages that an LLM can use as context. The framework handles serialization of user input and LLM responses, supporting rich content types such as text or blobs.
• Tool execution: methods become callable actions that an LLM can invoke on demand. The server translates the tool call into a method invocation, passes arguments, and returns the result in the MCP format.
• Resource serving: annotations expose static or dynamic content (e.g., files, database blobs) via a URI namespace. Clients can request these resources as part of the conversation or tool execution.
• Transport flexibility: The extension ships with both SSE (HTTP streaming) and STDIO transports, enabling deployment in cloud environments or local dev setups without code changes.

Real‑world use cases

• Code assistants: A tool that converts strings to lowercase or a prompt that generates language‑specific code snippets can be exposed with just a few lines of Java, allowing an LLM to assist developers directly within IDEs.
• Enterprise data access: By annotating methods that query a database or read from secure storage, an LLM can retrieve up‑to‑date information without exposing raw database credentials.
• Hybrid AI workflows: Combine prompt generation with tool execution to build multi‑step reasoning chains—e.g., a prompt proposes an algorithm, a tool fetches necessary data, and another prompt refines the solution.

Integration with AI workflows

The Quarkus MCP Server works hand‑in‑hand with the LangChain4j client library. Once the server is running, LangChain4j can discover available prompts, tools, and resources automatically, enabling dynamic chaining of LLM calls. Developers can therefore construct sophisticated AI pipelines—such as retrieval‑augmented generation or agentic workflows—without managing low‑level networking code.

Unique advantages

• Zero boilerplate: Annotated CDI beans eliminate the need for manual routing or JSON serialization.
• Type safety and CDI lifecycle: Leveraging Quarkus’ dependency injection guarantees that resources are properly initialized and cleaned up.
• Transport agnostic: The same code base works over HTTP/SSE or STDIO, making the server suitable for both cloud services and local development.
• Open‑source and community‑driven: Built on the Quarkus ecosystem, it benefits from continuous improvements, extensive testing, and a growing contributor base.

In summary, the Quarkus MCP Server empowers developers to expose LLM‑friendly APIs from their existing Java applications with minimal effort, fostering rapid prototyping and robust production deployments of AI‑enhanced services.