MCP vs A2A: Stop Building Agent Architectures Wrong

MCP vs A2A: Stop Building Agent Architectures Wrong

If you're wiring up AI agents in production right now, you've probably hit the same confusion I did: when do I use MCP, and when do I need A2A?

Turns out, they're not alternatives. They solve different problems at different layers of your stack. Mixing them up will wreck your architecture before you've shipped v1.

Let me break down what I wish someone had told me three months ago.

MCP: Your Agent Talks to Tools

Model Context Protocol (Anthropic's spec) is about agent-to-tool communication. Think of it as the interface layer between your LLM and the stuff it needs to do things.

When your agent needs to:

• Query a database
• Call an internal API
• Read from a file system
• Fetch customer records

...you're in MCP territory.

What MCP Actually Gives You

// MCP server exposes capabilities
const mcpServer = {
  tools: [
    {
      name: "query_customer_db",
      description: "Fetch customer by ID",
      inputSchema: { customerId: "string" }
    }
  ]
};

The protocol standardises how your agent discovers what tools exist, how to invoke them, and how results flow back. It's RPC with schema negotiation baked in.

Crucially: MCP doesn't care about agent autonomy. It's a request-response pattern. Your agent asks, the tool answers. Done.

A2A: Your Agents Talk to Each Other

Agent-to-Agent protocol (Google's answer to multi-agent coordination) operates at a completely different layer. This is about autonomous systems negotiating with each other.

When you need:

• A research agent to delegate to a summarisation agent
• A planning agent to coordinate with execution agents
• Agents to negotiate task ownership
• Asynchronous handoffs between agent workflows

...you're in A2A territory.

The Key Difference

A2A assumes both sides have agency. They're not calling dumb tools — they're collaborating with other intelligent systems that have their own goals, context, and decision-making.

# A2A coordination (conceptual)
Agent A -> Agent B: "Can you handle UK tax calculation?"
Agent B -> Agent A: "Yes, send me the transaction data"
Agent A -> Agent B: [structured payload]
Agent B -> Agent A: "Completed. Result: £2,450 VAT due"

Notice the back-and-forth? That's negotiation. MCP doesn't do that.

Why This Matters When You're Building

Here's where teams go wrong: they try to use MCP to wire agents together.

Don't do this:

# Anti-pattern: Agent-to-agent via MCP
mcp_server.register_tool(
    name="call_summarisation_agent",  # ❌ This is an agent, not a tool
    handler=lambda x: summarisation_agent.run(x)
)

Why does this break down?

• MCP is synchronous and blocking — agents need async coordination
• MCP has no concept of agent state or context handoff
• You lose the negotiation layer (what if the agent is busy? unavailable? needs clarification?)

Instead, use MCP to give each agent its own tools, then use A2A (or a message bus, or even HTTP with agent-aware semantics) to let them coordinate.

Better architecture:

┌─────────────────┐         ┌─────────────────┐
│   Agent A       │         │   Agent B       │
│                 │         │                 │
│  ┌───────────┐  │  A2A    │  ┌───────────┐  │
│  │ MCP Tools │  │ ◄─────► │  │ MCP Tools │  │
│  └───────────┘  │         │  └───────────┘  │
└─────────────────┘         └─────────────────┘
       │                           │
       │ MCP                       │ MCP
       ▼                           ▼
   [Database]                  [API]

Each agent gets its own MCP interface to tools. Agents talk to each other via A2A.

What to Do Tomorrow

If you're designing an agentic system:

1. Map your tool layer first — what external capabilities do agents need? Build MCP servers for those.
2. Identify agent boundaries — where does one agent's responsibility end and another's begin?
3. Choose your A2A transport — could be Google's spec, could be a message queue with agent-aware semantics. Just don't use MCP for it.
4. Keep agents dumb about each other's internals — they should coordinate via high-level intent, not RPC.

The two-layer stack isn't theoretical — it's the separation of concerns your system needs to scale beyond a proof-of-concept.

The Bottom Line

• MCP = agent ↔ tool (synchronous, request-response, capability exposure)
• A2A = agent ↔ agent (asynchronous, stateful, coordination)

Get this wrong and you'll end up refactoring your entire stack when you need to add agent #3. Get it right and your architecture stays clean as you scale.

If you're building this for real and need architecture help, teams doing serious AI automation and software development are already using this mental model.

Now go build something that doesn't fall over when you add more agents.

Comments

[Mateo Ruiz]

This is a distinction a lot of teams miss early on.

MCP and A2A often get discussed as competing approaches when they're really solving different architectural problems. MCP is great for capability access, but once agents need coordination, delegation, and state-aware collaboration, forcing everything through a tool-calling pattern starts creating unnecessary complexity.

The "agent ↔ tool" vs "agent ↔ agent" framing is probably the simplest way to explain it.

One trend we're seeing at IT Path Solutions is that many multi-agent projects initially overuse tool calls for orchestration because it's the quickest path to a prototype. As systems grow, teams usually end up introducing dedicated coordination layers, queues, or agent-to-agent communication patterns to keep responsibilities clean and scalable.

The separation of concerns point is key. Architectures tend to stay much healthier when tools expose capabilities and agents focus on decision-making rather than trying to do both jobs at once.