AI, decoded

What is the difference between MCP and A2A?

MCP connects an AI application to tools and context; A2A defines how separate agents communicate and coordinate work. They can be used together: an agent may accept a task through A2A, then use MCP-connected tools to carry it out.

· Chain of Thought

Level 5: Production agents · 5.1 Multi-agent systems

MCP (Model Context Protocol)Multi-Agent Systems

Two kinds of connection

An agent sometimes needs a specific operation: read a record, search documents or call a calculation tool. At other times, it needs another agent to take responsibility for a task and return a result. Those are different integration problems.

The A2A project’s comparison places MCP at the interface to tools and context, and A2A at the interface between agents. A2A supports collaboration without requiring agents to reveal their internal implementation. This is a useful way to choose an interface, rather than a rule that every system needs both.

In episode 31, Giovanna Carofiglio distinguishes tool access from agent-to-agent communication. Her discussion spans several protocols, including A2A and ACP. The enduring design question is what the other side of a connection is expected to do.

Follow a task across both interfaces

Illustrative example: an operations assistant needs a delivery plan for a customer order. It asks a separate logistics agent to prepare a plan. That agent may need time to gather constraints, ask for missing information and return a completed proposal. This is a task relationship you could expose through A2A.

Inside that logistics agent, a warehouse lookup and a carrier-rate calculator are individual capabilities. Those could be exposed through MCP. The outer assistant does not need to know the exact sequence of internal tool calls to receive the logistics result.

Decide what information crosses each boundary. The logistics agent might need destination and package details, but not the customer’s full account history. State whether the request is for a proposal or for an actual booking. A successful proposal must not be mistaken for authorization to purchase shipping.

Choose based on the integration contract

For a simple lookup, ask whether a tool interface already expresses the whole job. Adding a separately managed agent creates another component to operate and evaluate. For delegated work, specify how the caller learns whether the task is running, needs input, failed or finished.

The distinction is not absolute. The A2A guide also discusses representing A2A agents through MCP interfaces. An agent can appear as a callable capability to its client. Inspect the actual behavior and lifecycle you need rather than deciding from the protocol name alone.

Write down the expected inputs, allowed actions, result format and failure behavior before choosing an implementation. Use a sample task to check whether both sides understand completion the same way. See MCP versus custom integration for the separate question of how to expose tools.

Where it falls short

A communication protocol does not establish that an agent is competent, that its result is correct or that the caller has approved every possible action. Authentication and permissions still need to fit the actual system, and returned claims still need verification.

Test interrupted tasks, missing inputs and repeated requests as well as a clean success. Preserve enough task history to investigate a disputed outcome. Multi-agent reliability covers the handoff failures that a shared interface alone cannot resolve.

Hear it from the guest

“Agent to agent communication, this is trickier one.”
“It's much simpler for these agents to use tools and for tools to provide you a way like an API to be used by agents behind MCP servers.”

Quotes lightly edited to remove filler words.

Go deeper

From the conversation

This explainer is drawn from these episodes — each carries its full transcript.

Concepts in this explainer

Model Context Protocol (MCP)