MCP vs A2A: Building Bridges Between AI Agents

When Agents Talk and Tools Connect

Protocol chains govern how machines speak. When one AI agent needs to talk to another, or when an agent requires external tools, the communication pathway matters. Google’s Agent-to-Agent (A2A) protocol and Anthropic’s Model Context Protocol (MCP) represent two approaches to this problem.

We need these protocols because the future of AI isn’t a single godlike entity but specialized agents working together. In this blog, we’ll examine how A2A and MCP solve different aspects of agent communication and how they can complement each other.

The Core Architecture Difference

A2A creates a network of conversing agents. Each agent can function as both client and server, sending tasks and receiving results. Through standardized messages over HTTP, agents discover each other using Agent Cards (JSON manifests at well-known URLs) and collaborate across organizational boundaries.

{
  "name": "HR Agent",
  "description": "Handles employee onboarding paperwork.",
  "url": "http://hr-agent.example.com",
  "capabilities": { "streaming": true },
  "skills": [
    {
      "id": "onboard_docs",
      "name": "Onboarding Documents Collection"
    }
  ]
}