A2A vs MCP: The Two Protocols Behind Every Serious AI Agent System

MCP (Model Context Protocol) connects a single AI agent to tools, data, and context. It answers: how does my agent use a resource — read a file, query a database, call an API?

A2A (Agent2Agent) connects agents to other agents. It answers: how does my agent delegate work to another agent, and get results back?

MCP is vertical — an agent reaching down to its capabilities. A2A is horizontal — an agent reaching across to its peers. They are complementary, not competing. Both are open standards governed by the Linux Foundation, and most production systems run both.

Why this question keeps coming up

Ask around and you'll hear A2A and MCP discussed as if they were rivals — as though picking one is a strategic decision with a winner. It isn't. The two protocols do not overlap, and the reason people think they might is that both showed up around the same time, both are about connecting agents to something, and both are open standards with big names behind them.

But once you know what sits on the other end of each connection, the confusion dissolves permanently. That's what this piece is for.

What is MCP?

MCP — the Model Context Protocol — is an open standard for connecting an AI agent to tools, data sources, and context. When an agent needs to read a file, query a database, hit an API, or pull in a document, MCP is the layer that makes that possible in a standardized way rather than through bespoke integration.

Before MCP, every agent framework had its own notion of a "tool," and wiring an agent to a data source meant writing an adapter specific to both. MCP made that connection a protocol, so a tool exposed once works with any agent that speaks it.

MCP is what gives an individual agent its capabilities. An agent without MCP (or something like it) can think, but it cannot reach anything.

What is A2A?

A2A — Agent2Agent — is an open protocol for agent-to-agent communication. It lets independent agents discover each other, delegate tasks, and exchange results securely, regardless of which framework or vendor built them.

A2A was introduced by Google in April 2025 and contributed to the Linux Foundation, which now governs it as a vendor-neutral standard. It has since reached a stable 1.0 release with core data models frozen. Official SDKs ship for Python, JavaScript, Java, Go, and .NET, and the protocol is supported inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore, and Google ADK.

Under the hood it is deliberately unglamorous: HTTP for transport, JSON-RPC 2.0 for structured requests, Server-Sent Events for streaming updates, and OAuth 2.0 with JWTs for authentication. Nothing exotic — which is exactly why adoption moved fast.

The five things A2A is made of

• Agent Card — a JSON document describing an agent's identity, endpoint, authentication, and skills, published at /.well-known/agent-card.json so others can discover it.
• Task — the central, stateful unit of work, with a lifecycle you can track from submission to completion.
• Message — a single turn of communication between two agents.
• Parts — typed content inside a Message: text, file, or structured data.
• Artifact — the durable output a completed Task produces.

A client discovers an agent via its card, sends a Message to open a Task, follows that Task through its states, and collects Artifacts when it completes. Five nouns, one flow — that's most of the protocol.

A2A vs MCP: the comparison

MCP gives an agent hands.
A2A gives it colleagues.

How they layer in a real system

Picture a market-report system in two planes.

Horizontally, over A2A: a user asks an orchestrator agent for a report. The orchestrator delegates data-gathering to a research agent and chart-making to a visualization agent — discovering each by its Agent Card, authenticating with a scoped token, and streaming each Task to completion.

Vertically, over MCP: the research agent queries live data sources; the visualization agent calls a rendering tool. Each is reaching down to the capabilities it needs.

Results flow back up as Artifacts, and the orchestrator assembles the report. Two planes, each doing one job, combining into something neither could do alone.

When do you need both?

• Use MCP alone if your agent uses tools but never delegates to another agent. A single capable assistant with database access needs no A2A.
• Use A2A alone for a pure router that forwards tasks between agents and holds no tools of its own. Rare, but real.
• Use both when an agent needs to act on resources and collaborate with peers — which describes essentially every multi-agent system of any ambition.

The goal is a system as simple as the problem allows. Don't reach for A2A because multi-agent sounds impressive: every delegation adds a network hop and usually another model call. Reach for it when specialization or scale genuinely earns that overhead.

Why the pairing is the durable bet

Standards win when they compose. HTTP did not have to also be HTML — each did one job, and together they carried the web. A2A and MCP follow the same logic, which is why betting on the pair is safer than betting on any single framework.

Here's the part worth internalizing: an agent that speaks A2A exposes only its card and its skills. Everything behind that surface is private — which model it calls, which framework it runs on, which tools it reaches through MCP, how its logic is written. Replace any of those and every caller is unaffected, because none of them ever depended on it.

Models will improve. Frameworks will be replaced. A catalog of well-described agents that speak an open protocol stays callable through all of it.

Common mistakes

• Using A2A to reach a database. If the other end is a resource, it's MCP. Ask what's on the other end — that answers it every time.
• Using MCP to connect two agents. Wrapping an agent as a "tool" works until that agent needs to ask a clarifying question, run for ten minutes, or report progress.
• Treating A2A like a function call. It's a protocol for stateful, long-running, cross-trust collaboration.
• Trusting peers by default. An agent that speaks A2A is not automatically safe.