Model: openai/gpt-5.4
Generated: 2026-04-01
Book: Claude Code VS OpenCode: Architecture, Design and The Road Ahead
Chapter: 7 — MCP: The USB-C of AI
Token Usage: ~10,000 input + ~1,800 output (estimated)

7.3 MCP vs A2A

Once MCP became widely discussed, another protocol question immediately followed: if MCP is standardizing tool access, what standard will govern communication between agents themselves? That is where A2A, Google’s Agent-to-Agent Protocol, enters the picture. The easiest mistake is to treat MCP and A2A as rivals. They are not. They solve different coordination problems at different layers.

The shortest distinction is this:

• MCP = agent ↔ tools/data
• A2A = agent ↔ agent

MCP is primarily about vertical integration. An agent sits at the top and reaches downward into capabilities: tools, resources, prompts, files, APIs, search systems, databases, memory stores, documentation servers, or browsers. A2A is about horizontal communication. One agent delegates to another agent, negotiates work, tracks progress, and retrieves results.

That difference sounds simple, but it has major design consequences.

What MCP optimizes for

MCP assumes that the main intelligence sits in the client host. The server exposes capabilities, but it does not need to be a planner. It just declares what it can do and responds to requests. The host decides when to call a tool, which resource to read, which prompt to fetch, and how to combine those results into ongoing reasoning.

So MCP is usually shaped like request-response capability access. Even when it streams progress or supports richer interaction, the core relationship is still: the client is orchestrating and the server is exposing capabilities.

MCP also tends to be session-scoped. A client connects to a server, lists tools/resources/prompts, and uses them inside an ongoing session. State may exist, but it is usually subordinate to the host’s session lifecycle. The server is part of the environment, not a peer principal with its own explicit task queue and work contract.

This is why MCP feels natural for things like:

• search servers
• docs servers
• filesystem bridges
• browser automation
• design APIs
• local database tools

These are capabilities an agent uses. They are not independent workers with their own agenda.

What A2A optimizes for

A2A starts from a different assumption: the thing on the other side is not just a capability provider, but another agentic actor. That means the other side may have its own model, memory, planning loop, permissions, and execution environment. Communication therefore cannot be reduced to “call method X with JSON params.”

Instead, A2A emphasizes task delegation. One agent submits work to another. The receiving agent may accept, reject, defer, clarify, start working, stream status, and eventually complete the task. The protocol therefore needs an explicit task lifecycle, often represented as states such as:

submitted → working → completed

In practical systems there may be richer variants such as failed, canceled, blocked, or needs-input, but the core idea is that work has durable lifecycle semantics.

A2A also introduces the idea of Agent Cards. An Agent Card is a machine-readable description of an agent’s identity and capabilities: what it does, what inputs it expects, what constraints it has, and how to contact it. If MCP servers are capability endpoints, Agent Cards are more like service advertisements for autonomous workers.

This makes A2A better suited for patterns like:

• delegating research to a specialist agent
• handing off implementation to a coding agent
• asking a verification agent to validate a result
• coordinating multi-step workflows across organizational boundaries
• allowing agents from different vendors to collaborate without sharing the same internal runtime

In other words, A2A is built for coordination among intelligences, not just access to utilities.

Intelligence placement: the most important distinction

The most useful conceptual difference is where the intelligence primarily lives.

In MCP, the intelligence is mostly in the client. The server may be sophisticated internally, but from the protocol’s perspective it is a tool/data provider.

In A2A, the intelligence exists on both sides. The caller must decide whom to delegate to and how to interpret progress. The callee must decide how to execute the task and what intermediate states to expose.

That difference changes everything from trust to UX. In MCP, the host can usually flatten results into tool output. In A2A, the host often needs explicit task tracking, partial completion handling, cancellation, retries, and responsibility boundaries.

Why they are complementary

Once you see the vertical-versus-horizontal split, the false competition disappears. A capable future agent system will probably need both.

Imagine a software engineering workflow in 2026:

1. A coordinator agent receives a feature request.
2. It uses A2A to delegate architecture review to one agent, codebase exploration to another, and test strategy to a third.
3. Each of those agents, while doing its work, uses MCP to access tools and data: docs servers, GitHub search, browser tools, file tools, build systems, memory systems, design systems.
4. The coordinator aggregates the completed A2A task results and decides the next step.

This is the natural layered future:

• MCP for tool access
• A2A for agent coordination

One gives agents hands. The other gives them coworkers.

Comparison table

Design implications for coding agents

For coding agents, the practical lesson is straightforward. If you are designing a single-agent coding assistant, MCP should likely come first. A single agent becomes dramatically more useful when it can access standardized tools, resources, and prompts. That is the minimum viable extensibility layer.

But once your system evolves into orchestration, background specialists, or organization-level automation, MCP alone is insufficient. Passing every delegated subtask through tool calls is a conceptual mismatch. At that point, you need agent-native delegation semantics: ownership, progress, cancellation, and result return. That is where A2A becomes necessary.

The deeper lesson is that the industry is converging on a two-protocol architecture. One protocol connects an agent to the world of capabilities. Another connects the agent to a world of peers. That mirrors distributed systems history. We use one class of protocols for services consuming resources and another for services coordinating work. Agent ecosystems are arriving at the same separation.

So the right question is not “MCP or A2A?” The right question is: where does your current boundary lie? If the boundary is between an agent and a capability provider, MCP is the right abstraction. If the boundary is between one autonomous worker and another, A2A is the right abstraction.

The future is not a winner-take-all protocol war. The future is layered interoperability. MCP will standardize what agents can use. A2A will standardize how agents cooperate. Together, they define the control plane of the agentic software stack.