As AI agents become more capable, they need ways to connect with tools and collaborate with other agents.

This is where MCP and A2A come in.

Although they are often discussed together, they solve different problems.

🛠️ MCP: Agent-to-tool communication

The Model Context Protocol helps an AI agent connect to external tools, APIs and data sources.

For example, an agent could use MCP to access:

• A database • A GitHub repository • A calendar • A file system • A business application

🤝 A2A: Agent-to-agent communication

The Agent2Agent protocol provides a common way for independent AI agents to discover one another, delegate tasks, exchange information and share results.

For example:

A travel agent could communicate with a flight-booking agent, hotel agent and itinerary-planning agent.

The easiest way to remember the difference is:

MCP gives an agent tools. A2A gives an agent teammates.

They are complementary rather than competing technologies.

MCP can equip each specialist agent with the tools it needs, while A2A allows those agents to collaborate across frameworks, systems and organisations.

This interoperability will be important as we move towards ecosystems containing many specialised AI agents rather than one agent attempting to do everything.

🎥 Watch: Introduction to the Agent2Agent Protocol

📖 Read: Official A2A Protocol documentation

#A2A #MCP #Agent2Agent #ModelContextProtocol #AgenticAI #AIAgents #Interoperability #DeveloperTools