mcp-rag-server

# Multi-Protocol RAG Server (MCP + A2A) Advanced implementation of a Retrieval Augmented Generation (RAG) server that supports both Model Context Protocol (MCP) and Google A2A protocols. This dual-protocol architecture enables flexible integration with different AI systems while maintaining a unified RAG backend. [A2AJava](https://github.com/vishalmysore/a2ajava) is swiss knife of agentic applications in Java and Kotlin. It supports multiple protocols including Google A2A, MCP, and others. This project demonstrates how to build a powerful RAG search application that integrates Google's Agent-to-Agent (A2A) protocol with Spring Boot and MongoDB Atlas. ## Architecture Overview The server implements a unique dual-protocol approach: 1. **MCP (Model Context Protocol)** - Enables direct communication with MCP-compatible AI models 2. **A2A (Agent-to-Agent Java)** - Provides Java-based agent communication capabilities This architecture allows for: - Seamless integration with multiple AI protocols - Unified RAG backend serving different client types - Flexible deployment options - Protocol-specific optimizations ## Screenshots and Features ### A2A Protocol Integration #### Agent Chat Interface  *A2A protocol enabling agent-to-agent communication* #### Agent Events  *Event handling in the A2A protocol implementation* #### Agent Addition  *Interface for adding new agents to the system* ### MCP Protocol Integration #### Claude MCP Agent  *MCP protocol integration with Claude AI* #### MCP Conversation Flow   *Demonstration of MCP-based conversation flow* #### RAG Retrieval Process  *RAG retrieval process using the MCP protocol* ## Key Components - `A2ARagService`: Handles A2A protocol-specific RAG operations - `RagRetrivalController`: Manages RAG retrievals across both protocols - `YardShareA2ACardController`: Controls A2A-specific card interactions - `RAGConfiguration`: Configures RAG settings for both protocols ## Building and Running To build the project: ```bash mvn clean install ``` To run the server: ```bash java -jar target/mcp-rag.jar ``` ## Configuration The server can be configured through: - `application.yml`: Main application configuration - `tools4ai.properties`: AI-specific settings ## Benefits of Dual-Protocol Architecture 1. **Protocol Flexibility**: Support for both MCP and A2A allows integration with a wider range of AI systems 2. **Unified RAG Backend**: Single source of truth for knowledge retrieval 3. **Scalable Design**: Easy to add support for additional protocols 4. **Optimized Performance**: Protocol-specific optimizations while maintaining a common core ## License See [LICENSE](LICENSE) file for details.