A2A Protocol vs MCP: Which Agent Communication Protocol Fits Your Stack?

Picking between A2A and MCP feels confusing because both are “agent protocols” — but they don’t actually compete.

A2A (Agent-to-Agent) and MCP (Model Context Protocol) solve different layers of the same problem. MCP connects your AI model to tools and data. A2A lets agents talk to each other. You might need one, the other, or both depending on your setup.

Let’s break down what each protocol actually does, where they overlap, and how to choose.

What MCP Does: Tools and Data Access#

MCP was created by Anthropic and released in late 2024. It’s a standardized way for AI models to reach outside their context window and interact with external systems.

Think of MCP as a USB-C for AI tools. Instead of each AI coding tool writing its own integration for every database, API, or file system, MCP provides one uniform interface. Your Claude Code or Cursor agent talks to an MCP server, and that server handles the actual data fetching or action execution.

Real example: You ask your agent to “find all unfinished PRs and summarize them.” Without MCP, the agent needs GitHub API credentials, knows the GraphQL syntax, handles pagination — fragile and tool-specific. With MCP, there’s a GitHub MCP server. The agent sends a standardized request, the server handles the API, and the agent gets clean results back.

MCP’s core feature set:

• Resource access — read files, query databases, fetch web pages
• Tool execution — run GitHub APIs, send Slack messages, trigger CI/CD
• Prompt templates — reusable prompt patterns stored on the server side
• Security boundary — the server controls what the model can reach

If your use case is “I want my AI agent to do things in the real world” — read a spreadsheet, post to Jira, query your analytics DB — MCP is what you need.

What A2A Does: Agent-to-Agent Coordination#

A2A comes from Google, announced in April 2025 and now hosted under the Linux Foundation. It solves a different problem: agents talking to other agents.

While MCP is vertical (model → tool), A2A is horizontal (agent → agent). Your research agent finds a bug, drafts a fix, and hands it to your testing agent — that’s A2A territory.

Here’s how A2A works in practice:

Each agent publishes an Agent Card — a JSON file listing its capabilities, endpoints, and authentication requirements. When Agent A needs help from Agent B, it reads the card, picks the right communication method (JSON-RPC, gRPC, or HTTP), and sends a task request.

A2A supports:

• Task-oriented messaging — “review this PR” not “call this function”
• Streaming results — get progress updates as the task runs
• Push notifications — the responding agent pings you when done
• Multi-vendor — Claude Code agents, Gemini agents, and custom agents can all participate

The key insight: A2A agents stay autonomous. Agent A doesn’t control Agent B’s internals. It sends a task, gets results back, and both agents remain independent.

Where it shines: Multi-agent workflows where specialization matters. A planning agent breaks work into subtasks. A coding agent implements. A review agent checks quality. A deployment agent ships. Each operates independently, coordinated through A2A messages.

Where They Overlap — and Why That’s OK#

Here’s where people get confused. Both protocols use JSON-based messaging. Both are open standards. Both live in the same ecosystem of AI agent tools. But they’re complementary, not competing.

The overlap is minimal when you think about the direction of communication. MCP goes down (model → infrastructure). A2A goes sideways (agent → agent). You can and should use both in the same system.

A real multi-agent architecture:

1. Your orchestrator agent (running in Claude Code) uses MCP to query your project database and read code files
2. It identifies a bug, drafts a fix, then uses A2A to send the fix to a testing agent
3. The testing agent uses its own MCP connection to spin up a test environment and run the suite
4. Results come back through A2A to the orchestrator
5. The orchestrator uses MCP again to create a PR with the fix

Both protocols, working together, each handling what they’re built for.

Making the Call: Which Protocol Do You Need?#

Pick MCP first if:

• You’re building a single-agent system
• Your agent needs to read files, query APIs, or run shell commands
• You want a standardized way to connect LLMs to your existing tools
• You’re using Claude Code, Cursor, or Windsurf (all support MCP natively)

Pick A2A first if:

• You’re running multiple agents with different specializations
• You need agents to discover each other dynamically
• You’re building agent teams that coordinate on complex tasks
• Your agents run on different platforms (Claude Code + Gemini, for example)

Pick both if:

• You have multi-agent teams where each agent also needs tool access
• You’re building a full agent platform with specialized roles
• You want future-proof architecture — the industry is moving toward both standards

Summary#

Don’t overthink the choice. MCP isn’t “losing” to A2A or vice versa. They’re two layers of a complete agent communication stack.

MCP handles the boring but essential work of giving models safe, standardized access to tools and data. A2A handles the exciting but complex work of letting agents coordinate as independent teammates.

If you’re starting today, set up MCP first — most single-agent workflows need it more. Add A2A when your system grows beyond one agent and you need coordination. By then, both protocols will be even more mature and better integrated.

For hands-on practice, install the A2A Protocol skill for your agent and try building a simple two-agent workflow. You’ll see the difference immediately.