MCP vs A2A: Model Context Protocol vs Agent2Agent (2026 Guide)

📖 TL;DR

MCP (Model Context Protocol) connects one agent to tools, data, and APIs — a vertical link from agent down to capabilities.
A2A (Agent2Agent) connects agents to other agents — a horizontal link so independent agents can delegate work.
MCP is from Anthropic; A2A started at Google and is now a Linux Foundation project. They are complementary, not competitors.
Use MCP to give an agent its toolbox. Use A2A to let that agent hire other agents.
Most production stacks in 2026 run both: A2A between agents, MCP from each agent to its tools.

The Key Difference in One Sentence

MCP connects an agent to its tools. A2A connects an agent to other agents.

That is the whole comparison in a line. The two protocols sit on different axes of the same system.

MCP is vertical: it runs from a single agent down to the tools, files, and APIs it can call. Think of it as the agent's toolbox.

A2A is horizontal: it runs sideways, between peer agents that each have their own tools and reasoning. Think of it as agents hiring each other.

So "MCP vs A2A" is rarely an either/or pick. Once you understand the axes, the question becomes where each one belongs in your stack — which is what the rest of this guide covers.

What Is MCP (Model Context Protocol)?

MCP is an open standard from Anthropic, released in November 2024, for connecting AI models to external systems.

An MCP server exposes three things to a model: tools (functions the model can call), resources (data it can read), and prompts (reusable templates).

The model is the client. It discovers what a server offers, picks a tool, and calls it with arguments — all over JSON-RPC 2.0.

The point is to kill custom glue code. Instead of writing a one-off integration for every API, you expose a standard MCP surface once and any MCP-aware client can use it.

If you are new to it, start with What Is the Model Context Protocol? and how MCP tool calling works.

MCP answers one question: how does this agent reach the outside world? Files, databases, GitHub, Slack, your own REST API — all of it becomes callable through a single protocol.

What Is A2A (Agent2Agent)?

A2A is an open protocol for agent-to-agent communication. Google announced it in April 2025 and donated it to the Linux Foundation in June 2025, with backing from 50+ partners.

MCP assumes the client is a model talking to tools. A2A assumes both sides are full agents — each with its own model, memory, and tools.

A2A lets a "client agent" hand a task to a "remote agent" and get results back, without either side exposing its internal logic.

That last part matters. A2A agents are opaque — they collaborate as black boxes. The client agent does not see the remote agent's tools or prompts, only the task and its output.

The core building blocks are the Agent Card (a public capability profile), Tasks (units of work with a lifecycle), Messages, and Artifacts (the results a task produces).

A real example: a hiring agent asks a sourcing agent to find candidates, then asks a scheduling agent to book interviews. Three separate agents, possibly three vendors, coordinating over A2A.

A2A answers a different question than MCP: how do independent agents delegate work to each other?

MCP vs A2A: Side-by-Side Comparison

	MCP	A2A
Connects	Agent → tools & data	Agent ↔ agent
Axis	Vertical (down to capabilities)	Horizontal (across peers)
Origin	Anthropic (Nov 2024)	Google → Linux Foundation (2025)
Unit of work	Tool call	Task (with a lifecycle)
Discovery	tools/list, resources/list	Agent Card (JSON profile)
Other side is	A concrete tool surface	An opaque, autonomous agent
Transport	JSON-RPC 2.0 over STDIO or Streamable HTTP	JSON-RPC 2.0 over HTTP(S), SSE, webhooks
Best for	Giving one agent capabilities	Coordinating many agents

The rows that matter most are the first two. Everything else follows from "agent-to-tool" versus "agent-to-agent".

MCP vs A2A: Architecture and Transport

Both protocols speak JSON-RPC 2.0, so the wire format feels familiar across the two. The shape of what they exchange is where they split.

MCP architecture

An MCP host (like Claude Desktop) runs one or more MCP clients. Each client holds a session with one MCP server.

Transport is either STDIO for local servers or Streamable HTTP for remote ones. The exchange is request/response: list tools, call a tool, get a result.

For a deeper protocol breakdown, see MCP vs REST API and MCP vs function calling.

A2A architecture

A2A has a client agent and a remote agent, both running as HTTP services. The client sends a task; the remote agent works it and returns artifacts.

Because agent tasks can be long-running, A2A leans on Server-Sent Events for streaming and webhook push notifications for updates that arrive minutes — or hours — later.

A task moves through states: submitted, working, input-required, completed, failed, or canceled. That lifecycle is something MCP's single tool call does not model.

Key nuance: MCP optimizes for a fast, synchronous tool call. A2A optimizes for a delegated task that may take a while and report back asynchronously. The transport choices flow directly from that.

Discovery: tools/list vs the Agent Card

Discovery is the cleanest way to feel the difference between the two protocols.

In MCP, a client asks a server "what can you do?" by calling tools/list. It gets back concrete tools with names, descriptions, and JSON input schemas.

The model then reasons over that list and calls a specific tool. The server's capabilities are transparent by design.

In A2A, discovery happens through the Agent Card — a JSON document an agent publishes at a well-known URL (typically /.well-known/agent-card.json).

The Agent Card describes the agent's skills, endpoint, auth requirements, and supported modes — but not its internal tools. A client agent reads the card to decide whether to delegate, not which function to invoke.

So MCP discovery says "here are my functions." A2A discovery says "here is what I can take on." One exposes a toolbox; the other advertises a hire-able specialist.

When you build an MCP server, that tools/list response is the contract a model depends on. Vague names or weak schemas quietly break tool selection.

💡 The fastest way to check that contract is to run your server against a real model and watch which tools it picks. Test any MCP server free →

When to Use MCP vs A2A

Pick based on what is on the other end of the connection.

Reach for MCP when…

You want one agent to read a database, hit an API, or touch the filesystem.
The capability is a function, not an autonomous actor.
You need fast, synchronous calls with structured inputs and outputs.
You are wrapping your own services so any MCP client can use them.

Reach for A2A when…

You have multiple agents that should delegate work to each other.
Those agents are built on different frameworks or owned by different teams or vendors.
A unit of work is a long-running task, not a single call.
You want agents to collaborate without exposing each other's internals.

A quick gut check: if the thing you are connecting to cannot reason on its own, it is an MCP tool. If it can, it is an A2A agent.

Building a multi-agent system today? Pair this with multi-agent MCP with CrewAI and LangChain for the orchestration side.

Why You'll Often Use Both Together

The framing "MCP vs A2A" sells the wrong story. In real systems, they stack.

Google said as much when it launched A2A: it is positioned as complementary to MCP, not a replacement.

Here is the mental model. A2A is how agents coordinate. MCP is how each agent gets work done.

Walk through one task. A user asks a "travel planner" agent to book a trip.

The planner agent uses A2A to delegate flights to a flight agent and hotels to a hotel agent.
The flight agent uses MCP to call an airline API and a payments tool.
The hotel agent uses MCP to query a booking database and a maps service.
Results flow back over A2A as artifacts, and the planner assembles the itinerary.

A2A handles the conversation between agents. MCP handles each agent's grip on the real world. Remove either and the system stops working.

That is why I treat them as two layers of the same architecture, not rival standards. Most serious agent platforms in 2026 ship support for both.

Building the MCP layer of your agent stack?

Test your MCP server against real models in the browser before any agent — or any other agent — depends on it.

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Frequently Asked Questions

Does A2A replace MCP?+

No. They operate on different axes. MCP connects an agent to tools and data; A2A connects agents to each other. Google explicitly positions A2A as complementary to MCP, and most agent stacks use both.

Who created MCP and A2A?+

Anthropic released MCP in November 2024. Google announced A2A in April 2025 and donated it to the Linux Foundation in June 2025, where it is now a vendor-neutral open project.

What is an Agent Card in A2A?+

An Agent Card is a JSON document an agent publishes at a well-known URL describing its skills, endpoint, and auth requirements. Other agents read it to decide whether to delegate a task — it is A2A's discovery mechanism, similar in spirit to MCP's tools/list.

Can I use MCP and A2A in the same system?+

Yes, and most production systems do. A2A handles coordination between agents while each agent uses MCP to call its own tools and data sources. They form two layers of one architecture rather than competing choices.

Is A2A the same as multi-agent frameworks like CrewAI?+

Not quite. Frameworks like CrewAI or LangGraph orchestrate agents inside one application. A2A is an open wire protocol for agents to communicate across frameworks, teams, and vendors — including agents the framework did not build.