Building a Multi-Protocol RAG System: Bridging A2A and MCP

a2ajava library is “swiss knife” for building agentic applicaiton in multiple languages such as java and kotlin and supports multiple protocol , code for the entire article is here. This server will be: ✅ Cross-protocol: Supports both MCP (Model Context Protocol) and Google A2A (Agent-to-Agent)

As the A2A (Agent-to-Agent) ecosystem continues to grow, one of the challenges we face is integrating different AI protocols while maintaining robust knowledge retrieval capabilities. I'm excited to share a solution that bridges this gap by combining A2A with the Model Context Protocol (MCP) in a unified RAG (Retrieval Augmented Generation) system.

The Challenge

Many organizations are running multiple AI systems, some using A2A for agent-to-agent communication and others using MCP for direct model interactions. This often leads to:

• Fragmented knowledge bases
• Inconsistent retrieval mechanisms
• Complex maintenance of multiple systems
• Integration challenges

The Solution: A Unified Multi-Protocol RAG System

I've developed a system that seamlessly integrates both A2A and MCP protocols, allowing for unified knowledge retrieval while maintaining protocol-specific optimizations. Here's how it works:

A2A Integration

The system implements A2A protocol through several key components:

1. A2ARagService: Handles RAG operations specifically for A2A agents
2. YardShareA2ACardController: Manages card-based interactions between agents
3. Custom Action Handlers: Processes specific A2A actions (LawnShareAction, YardShareAction)

Here's what the A2A interface looks like in action:

Agents can be dynamically added to the system:

The event system ensures smooth agent-to-agent communication:

MCP Integration

The MCP side of the system provides direct model interaction capabilities:

1. RagRetrivalController: Unified retrieval endpoint supporting MCP
2. RAGConfiguration: Flexible configuration for different model contexts
3. Anthropic Claude Integration: Demonstrating MCP with Claude

Here's the MCP agent in action:

The conversation flow maintains context and supports complex interactions:

The RAG retrieval process in action:

Technical Implementation

Core Components

// Key service for A2A RAG operations
@Service
public class A2ARagService {
    // Handles RAG operations for A2A protocol
}

// Controller for unified retrieval
@RestController
public class RagRetrivalController {
    // Supports both A2A and MCP retrievals
}

Configuration

The system uses a flexible configuration approach:

1. application.yml for core settings
2. tools4ai.properties for AI-specific configurations

Key Features

1. Protocol Abstraction Layer

   • Common interface for both protocols
   • Protocol-specific optimizations
   • Unified retrieval endpoints

2. Dynamic Agent Management

   • Runtime agent addition/removal
   • Flexible agent configurations
   • Event-driven communication

3. Unified RAG Backend

   • Shared knowledge base
   • Consistent retrieval mechanisms
   • Protocol-aware response formatting

Benefits for the A2A Community

1. Seamless Integration

   • Easy integration with existing A2A systems
   • Support for MCP without breaking A2A functionality
   • Gradual migration path for legacy systems

2. Enhanced Capabilities

   • Access to both A2A and MCP models
   • Unified knowledge retrieval
   • Consistent response formatting

3. Future-Proof Architecture

   • Easy to add new protocols
   • Scalable design
   • Maintainable codebase

Getting Started

1. Clone the repository
2. Run the application:

   java -jar mcp-rag.jar

Future Directions

1. Enhanced Protocol Support

   • Adding support for more AI protocols
   • Improved protocol negotiation
   • Dynamic protocol switching

2. Advanced RAG Features

   • Multi-modal retrieval
   • Context-aware optimizations
   • Improved ranking algorithms

3. Community Integration

   • Plugin system for custom protocols
   • Shared knowledge bases
   • Community-driven improvements

Conclusion

This multi-protocol RAG system demonstrates how we can bridge different AI protocols while maintaining the strengths of each. For the A2A community, this means being able to leverage both A2A and MCP capabilities without compromising on either. The system provides a blueprint for future protocol integrations while maintaining the robust agent-to-agent communication that makes A2A special.

I welcome contributions and feedback from the community. Together, we can build a more connected and capable AI ecosystem.

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Share your thoughts and experiences in the comments below. How are you handling multiple protocols in your A2A implementations?

Code for the article is here