MCP and A2A: Two bright modular futures for AI

Let’s compare model context protocol and Agent2Agent

To say that AI is a field undergoing rapid innovation and expansion would be an extreme understatement. In recent years it’s been hard to keep up with the deluge of new models, capabilities, and tools. At the moment there are perhaps fewer brighter rising stars than a pair of new open standards for AI development.

In this article we’ll introduce and explore the concepts of model context protocol (MCP) servers and the Agent2Agent (A2A) protocol, compare and contrast the two approaches, and help you determine what’s right for you and your team.

Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open protocol that seeks to standardize the tools and capabilities that AI systems can rely on. Prior to MCP you could build custom tools into an AI application using tooling like Microsoft Semantic Kernel or LangChain through custom code and connectors. This would allow you to marry together large language models and your own data or custom toolsets.

This approach was effective and helped us build custom retrieval augmentation generation (RAG) solutions like a web chat agent or full-blown AI orchestration solutions like a hobby project I carried out to build an AI-powered dungeon master. However, while these agents were capable, their capabilities were contained within the AI application on its own.