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MCP Server and Client in Java

Introduction

A2AJava is a powerful toolkit that serves as a Swiss Army knife for building agentic applications. It provides a unified framework for creating both A2A (Agent-to-Agent) and MCP (Model Context Protocol) servers, along with their respective clients. This versatility makes A2AJava an excellent choice for developing interconnected AI-powered applications.

Code for this article is here

What Makes A2AJava Special?

  1. Dual Protocol Support:

    • Implements Google's Agent-to-Agent (A2A) protocol for AI agent communication
    • Supports Model Context Protocol (MCP) for AI model interactions
    • Single codebase can handle both protocols seamlessly

  2. Rapid Development:

    • Quick server setup with minimal boilerplate
    • Ready-to-use client implementations
    • Extensible architecture for custom functionality

  3. Agentic Mesh Capabilities:

    • Java clients can be combined to create complex agent networks
    • Enables creation of distributed AI systems
    • Flexible communication patterns between agents

Tutorial: Building a Library Management System

In this tutorial, we'll create a simple library management system using A2AJava. Our system will demonstrate both A2A and MCP capabilities by implementing:

  • A library service server
  • A2A client for agent-to-agent communication
  • MCP client for model interactions

Project Structure 

src/
  main/
    java/
      org/
        example/
          client/
            A2AClient.java    - Agent-to-Agent protocol client
            MCPClient.java    - Model Context Protocol client
          server/
            Application.java  - Main server application
            LibraryService.java - Library management logic
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Setting Up the Server

  1. Build the project: 
mvn clean install
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  1. Run the server: 
mvn spring-boot:run
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The server will start on http://localhost:7860

Using the Clients

Both A2A and MCP clients are available in the org.example.client package:

  1. A2AClient: Use this when you need agent-to-agent communication:

    • Book recommendations between agents
    • Collaborative filtering
    • Inter-library communication

  2. MCPClient: Use this for AI model interactions:

    • Natural language processing of book descriptions
    • User preference analysis
    • Content categorization

Creating an Agentic Mesh

By combining multiple clients, you can create a sophisticated network of agents that work together. For example:

  • Recommendation agents communicating via A2A
  • Analysis agents using MCP for model interactions
  • Coordination agents managing the workflow

Conclusion

A2AJava provides a robust foundation for building agentic applications. Whether you're creating a simple service or a complex mesh of AI agents, the framework's dual protocol support and ready-to-use clients make development efficient and straightforward.

This tutorial demonstrated basic setup and usage, but A2AJava's capabilities extend far beyond this example. The combination of A2A and MCP support makes it a versatile choice for any AI-powered application.

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