Everyone Is Asking 'A2A or MCP' — The Question Itself Is Wrong

A familiar scene

Your team is in a tech review meeting. Someone pulls up a slide: “Agent Communication — Protocol Selection.” The discussion immediately splits into two camps. One side insists on MCP. The other argues for A2A. The meeting ends with no decision and an action item to “do more research.”

This scene plays out weekly in engineering teams building agent systems. It’s understandable — two acronyms that both deal with “agents” and “protocols” naturally invite comparison. But the comparison itself is a category error, like asking whether you should use HTTP or SQL. They operate at different layers, solve different problems, and in a well-architected system, you need both.

What MCP solves

The Model Context Protocol (MCP), introduced by Anthropic in late 2024, standardizes how an AI agent connects to external tools and data sources. Before MCP, every tool integration was bespoke — custom code to call a database here, a different wrapper for a search API there, yet another adapter for a file system.

MCP gives agents a standard interface for acting on the world. Think of it as USB-C for agent capabilities: one protocol, many peripherals. An MCP server exposes tools (functions the agent can call), resources (data the agent can read), and prompts (reusable instruction templates). The agent connects via an MCP client, discovers what’s available, and starts working.

The numbers speak for themselves. As of early 2026, the MCP ecosystem has exploded — thousands of MCP servers cover everything from GitHub and Slack to Postgres and Stripe. The npm @modelcontextprotocol/sdk package alone has crossed 9.7 million monthly downloads. Anthropic, OpenAI, Google, Microsoft, and essentially every major AI lab has adopted MCP as the standard for tool integration.

The key insight: MCP is agent-to-tool. It extends an agent’s capabilities outward. The agent is the client; the tool is the server.

What A2A solves

The Agent-to-Agent Protocol (A2A), launched by Google in April 2025 and reaching v1.0.0 in early 2026, solves a fundamentally different problem: how agents discover, communicate with, and delegate tasks to other agents.

Without A2A, multi-agent collaboration is a mess of proprietary APIs and hardcoded integrations. Agent A from vendor X cannot talk to Agent B from vendor Y. There’s no standard way to discover what an agent can do, negotiate task parameters, or stream results back.

A2A introduces several key primitives:

• Agent Cards — JSON metadata documents (typically at /.well-known/agent.json) that describe an agent’s capabilities, authentication requirements, and supported interaction modes. Think of it as DNS for agents.
• Tasks — The core unit of work in A2A, with well-defined lifecycle states (submitted, working, input-required, completed, failed, canceled).
• Messages and Parts — Structured communication payloads that support text, files, and structured data.
• Streaming — Server-Sent Events (SSE) for real-time progress updates on long-running tasks.

With 150+ organizations backing the protocol — including Salesforce, SAP, Atlassian, and ServiceNow — A2A is rapidly becoming the interoperability standard for agent ecosystems.

The key insight: A2A is agent-to-agent (or system-to-agent). It defines how agents are discovered and invoked. The agent is the server; other agents or systems are the clients.

The distinction, in one table

The analogy that sticks: MCP gives an agent things to do. A2A gives an agent a way to be reached. One is capability extension; the other is communication infrastructure.

Where they sit in the stack

┌─────────────────────────────────────────────┐
│                User / System                 │
└──────────────────────┬──────────────────────┘
                       │
              ┌────────▼────────┐
              │   A2A Protocol   │  ← Agent discovery & delegation
              │  (agent-to-agent)│
              └────────┬────────┘
                       │
              ┌────────▼────────┐
              │  Agent Runtime   │  ← LangGraph, CrewAI, AutoGen...
              │   (your agent)   │
              └────────┬────────┘
                       │
              ┌────────▼────────┐
              │   MCP Protocol   │  ← Tool integration
              │  (agent-to-tool) │
              └────────┬────────┘
                       │
         ┌─────────────┼─────────────┐
         ▼             ▼             ▼
     ┌───────┐    ┌────────┐   ┌─────────┐
     │  DB   │    │  API   │   │  Files  │
     └───────┘    └────────┘   └─────────┘

A2A sits above the agent; MCP sits below it. They are different layers, not alternative choices.

Adoption timeline

Both protocols are moving fast. The ecosystem is converging, not fragmenting.

Why “both” is the only right answer

Consider a realistic scenario: a customer support system where a Routing Agent receives a user request, determines the best specialist agent, and delegates the task.

User ──A2A──▶ Routing Agent ──A2A──▶ Billing Agent ──MCP──▶ Database
                                           │
                                      MCP──▶ Stripe API
                                           │
                    User ◀──A2A── Routing ◀─A2A─┘

1. The user’s message arrives at the Routing Agent. It uses A2A to discover available specialist agents and their capabilities via Agent Cards.
2. The Routing Agent delegates the task to a Billing Agent via A2A, sending a task with the user’s query.
3. The Billing Agent needs to look up the customer’s account. It calls a billing database tool via MCP.
4. The Billing Agent processes the result and returns a response to the Routing Agent via A2A.
5. The Routing Agent sends the answer back to the user.

In this flow, A2A handles all the inter-agent coordination — discovery, delegation, status updates, response delivery. MCP handles the actual work — connecting to databases, calling APIs, reading files. Remove either protocol and the system breaks.

This isn’t a theoretical exercise. Every production multi-agent system we’ve seen follows this pattern: agents need to coordinate with each other (A2A) and interact with tools (MCP). The protocols are complementary layers in the same stack.

Practical guidance by role

If you’re building an individual agent, start with MCP. Your agent needs to do useful things — search the web, query databases, call APIs. MCP’s tool integration is the foundation. Add A2A when you need your agent to be discoverable and callable by other agents or orchestration systems.

If you’re building agent infrastructure — orchestration platforms, agent marketplaces, enterprise agent networks — A2A is essential from day one. Your system’s value depends on agents being able to find and talk to each other. MCP capability will come from the individual agents you host.

If you’re evaluating these protocols for a tech decision, stop framing them as alternatives. Your architecture diagram should have both. MCP at the tool integration layer. A2A at the agent communication layer. The real question isn’t “which one” — it’s “how do these layers interact in our system?”

The AI agent ecosystem is converging on a standard stack. MCP and A2A are two foundational layers of that stack. Choosing between them is like choosing between TCP and HTTP — it reveals a misunderstanding of the architecture, not a genuine tradeoff.

Build with both. Your agents will be more capable and more interoperable for it.