Model Context Protocol MCP Client-Server Pattern for AI Building an Interactive Voice Agent

Building Voice-Enabled MCP Client–Server Orchestration with Spring Boot & Google ADK

Building upon the MCP Client-Server Architectural Foundation, this guide explores the implementation of Real-Time Chat & Voice Orchestration. We transition from a CLI-based interaction model to a fully multimodal experience, leveraging the Gemini Multimodal Live API. By integrating Spring Boot and the Google AI SDK, we establish a low-latency, two-way communication loop that enables AI agents to process voice commands and respond with natural, conversational speech.

Last Updated: Jan 20, 2026 (Originally Published: Dec 12, 2025) MCP AI-Agent Spring-Boot Web-Socket
3 min read

Expanded Architectural Ecosystem

To support real-time audio streaming and interactive visualization, we introduce two critical components to the existing MCP ecosystem:

  • mcp_orchestrator_springboot_client
  • react_web

Spring Boot Orchestrator (Backend)

The mcp_orchestrator_springboot_client serves as the central nervous system of the application. Beyond managing standard MCP tool connections, it facilitates the complex interaction between the user and the Google AI SDK.

  • Stream Management It handles high-frequency WebSocket frames, routing live audio data from the browser to the LLM.
  • Multimodal Processing It orchestrates voice-to-text transcription and text-to-voice synthesization in a unified reasoning loop.
  • State Synchronization Maintains the ModelContext across asynchronous events, ensuring the agent remains aware of available MCP tools during live conversation.

Interactive React Dashboard (Frontend)

The react_web module provides the visual interface required for professional multimodal engagement. It is optimized for low-latency audio capture and real-time status updates.

  • Audio Buffer Management Utilizes browser APIs to capture and stream high-quality audio data to the backend via persistent WebSockets.
  • Live Transcription Displays real-time text feedback as the agent processes incoming voice data, enhancing user trust and clarity.
  • Conversation History Provides a visual timeline of the interaction, including tool invocations (e.g., email sent, billing authorized) triggered by the agent via MCP.

Architectural Note This setup leverages Server-Sent Events (SSE) for MCP server communication while utilizing WebSockets for the voice-streaming loop. This hybrid approach ensures that tool discovery remains lightweight and standardized, while the user interaction remains responsive and interactive.

Deployment Tip: For production-grade voice agents, ensure your Spring Boot backend is deployed with sufficient resources to handle concurrent audio streams and maintain WebSocket persistence. Cloud Run with WebSockets enabled is a recommended target for these orchestration workloads.

Ready to explore the full implementation?
Review the Full Source Code on GitHub or return to the MCP Overview to explore more integration patterns.