A2A and MCP

When building agentic applications, two key protocols serve different but complementary purposes:

1. A2A (Agent2Agent Protocol): Enables communication between agentic applications
2. MCP (Model Context Protocol): Connects agents to tools, APIs, and resources

Two Complementary Protocols

Model Context Protocol (MCP)

The Model Context Protocol focuses on enabling LLMs and agents to interact with tools, plugins, and resources:

• Defines how models and tools exchange information
• Provides structured ways to represent tools and their capabilities
• Enables tools to return rich, multimodal content to models

MCP is primarily concerned with extending the capabilities of a single agent by connecting it to external tools and resources.

Agent2Agent Protocol (A2A)

The A2A Protocol focuses on enabling communication between different agentic applications:

• Defines how agents discover and communicate with each other
• Enables asynchronous, stateful interactions between agents
• Supports multimodal content exchange
• Allows agents to collaborate on complex tasks

A2A is primarily concerned with enabling collaboration between different agents, each with their own unique capabilities.

How They Work Together

A typical agentic application might use both protocols:

1. Internal tooling (MCP): The agent uses MCP to connect to its own tools, APIs, and resources
2. External collaboration (A2A): The agent uses A2A to collaborate with other agents when it needs specialized capabilities

In this architecture:

• MCP tools extend the agent's own capabilities
• A2A protocol allows the agent to collaborate with other agents

Key Differences

When to Use Each

Use MCP when:

• You need to extend an agent with specific tools or APIs
• The operation is relatively simple and doesn't require another agent's reasoning
• You have tools that need to be shared across multiple agents
• The operation is expected to be quick and synchronous

Use A2A when:

• You need specialized capabilities best provided by another agent
• The task requires complex reasoning or domain expertise
• The operation might be long-running or require multiple turns
• You want to enable collaboration between agentic applications

Example Scenario

Consider a travel planning assistant:

• MCP: Used to connect to flight search APIs, hotel booking systems, weather services
• A2A: Used to communicate with specialized agents like:
  • A local expert agent that knows about attractions and customs
  • A cuisine expert agent that can recommend restaurants
  • A language tutor agent that can help with phrase translations

By using both protocols appropriately, agentic applications can have the best of both worlds: direct access to tools when needed and collaborative capabilities with other agents for more complex tasks.