A Study on the MCP x A2A Framework for Enhancing Interoperability of LLM-based Autonomous Agents

TL;DR

This paper analyzes the integration of Google's Agent-to-Agent protocol and Anthropic's Model Context Protocol to improve interoperability and collaboration among heterogeneous LLM-based autonomous agents.

Contribution

It presents an integrated framework combining A2A and MCP protocols to enhance agent interoperability and external tool integration.

Findings

Demonstrates improved agent collaboration efficiency

Provides a unified protocol framework for heterogeneous agents

Highlights potential for scalable AI system development

Abstract

This paper provides an in-depth technical analysis and implementation methodology of the open-source Agent-to-Agent (A2A) protocol developed by Google and the Model Context Protocol (MCP) introduced by Anthropic. While the evolution of LLM-based autonomous agents is rapidly accelerating, efficient interactions among these agents and their integration with external systems remain significant challenges. In modern AI systems, collaboration between autonomous agents and integration with external tools have become essential elements for building practical AI applications. A2A offers a standardized communication method that enables agents developed in heterogeneous environments to collaborate effectively, while MCP provides a structured I/O framework for agents to connect with external tools and resources. Prior studies have focused primarily on the features and applications of either A2A or MCP individually. In contrast, this study takes an integrated approach, exploring how the two protocols can complement each other to address interoperability issues and facilitate efficient collaboration within complex agent ecosystems.