MCP vs A2A The Competing Protocols Shaping AI Agent Communication

As AI agents become more prevalent, the need for standardized communication protocols has grown. Two major players have emerged: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol. While they share similarities, they address distinct challenges in the AI ecosystem.

MCP: The 'USB-C' of AI

Introduced last year, MCP is designed to connect AI systems with external data stores, APIs, and tools. It uses a client-server architecture with JSON-RPC over Stdio, HTTP, or SSE. MCP's strength lies in standardizing how models interact with existing resources like databases or Kubernetes clusters.

Here's a high-level look at MCP's architecture. Credit: modelcontextprotocol.io

However, MCP has also introduced new security threats, and early trials show AI agents still have a long way to go in reliability.

A2A: The 'Ethernet' for Agents

Google's A2A, revealed at I/O 2025, focuses on agent-to-agent communication. Like MCP, it uses JSON-RPC, HTTP, and SSE but is tailored for interactions between specialized agents. The protocol includes:

Discovery phase: Agents exchange 'business cards' detailing capabilities and authentication
Task execution: Roles (client/server) are context-dependent
Progress tracking: Uses task IDs for longer-running jobs

Here's a breakdown of how Google's A2A Protocol works.

A2A has gained support from major players like Accenture, Atlassian, and Workday. Google has also announced plans to donate A2A to the Linux Foundation.

Key Differences

MCP connects models to external resources
A2A enables communication between agents
Both are backed by tech giants but serve different purposes

The Road Ahead

With the AI ecosystem still evolving, these protocols may face competition as agentic systems mature. Standards bodies' slow pace could also lead to fragmentation if disagreements arise over features or implementation.