Quarkus + MCP = Agentic

What is Quarkus + MCP = Agentic?

This project uses Quarkus and the Model Context Protocol (MCP) to build an agentic application that orchestrates multiple MCP servers (Brave Search, Google Maps, Slack, filesystem) via Quarkus + LangChain4j. It is intended for developers who want to experiment with agentic AI using Java and a modular tool ecosystem.

How to use Quarkus + MCP = Agentic?

Install dependencies (node, npm, and a container runtime like Podman or Docker for telemetry). Create a .env file with API keys for Brave, OpenAI, Google Maps, and Slack. Create a playground directory for the filesystem MCP server. Run the application in dev mode using ./mvnw compile quarkus:dev and access the chat UI at http://localhost:8080 or the Dev UI at http://localhost:8080/q/dev/ to issue prompts.

Key features of Quarkus + MCP = Agentic

• Integrates multiple MCP servers (Brave, Google Maps, Slack, filesystem) in one app.
• Uses LangChain4j for LLM orchestration and tool calling.
• Provides a chat UI and a Dev UI for live interaction.
• Built-in telemetry with Grafana (requires container environment).
• Supports agentic reasoning for multi-step tasks.
• Packaged as a standard Quarkus application (JAR or native executable).

Use cases of Quarkus + MCP = Agentic

• Plan a team lunch: search for restaurants, check dietary needs, send Slack invitations, and generate a calendar file.
• Search the web with Brave and combine results with location data from Google Maps.
• Read and write files on the local filesystem (e.g., create ICS calendar files).
• Use the Dev UI to debug and inspect the LLM’s reasoning and tool usage.