MCP vs A2A: What's the Difference and Which Should Your Agent Use?

MCP and A2A both show up in conversations about AI agent infrastructure, and both are described as "protocols for AI agents." But they solve completely different problems at different layers of the stack. Picking the wrong one — or assuming you have to pick — is a common mistake. Here's the clear version.

The one-sentence distinction

MCP (Model Context Protocol) connects an AI agent to external tools and data sources — databases, APIs, documentation, file systems.

A2A (Agent-to-Agent) connects one AI agent to another AI agent — enabling agents to delegate tasks, coordinate work, and communicate about their capabilities and status.

MCP is vertical: agent ↔ tool. A2A is horizontal: agent ↔ agent. They operate at different layers and are designed to work together, not compete.

What MCP does

MCP is a client-server protocol. The "client" is an AI agent (Claude, Cursor, a custom agent you built). The "server" is a tool or data source that exposes capabilities the agent can use: query a database, search documentation, read a file, call an API, submit a form.

When an agent connects to an MCP server, it discovers what tools are available through server/discover, then calls those tools with structured inputs and receives structured outputs. The agent reasons about the outputs and decides what to do next.

MCP is designed for the case where a human-facing agent needs to reach out to external systems to complete a task. The agent is the decision-maker; the MCP servers are its hands.

Examples of what you'd build with MCP:

• A documentation search tool that an agent queries to answer questions about a product
• A database connector that an agent uses to look up customer records
• A code execution sandbox that an agent calls to run and test code
• A CRM integration that an agent uses to create leads and update opportunities

What A2A does

A2A is Google's protocol for agent-to-agent communication. It defines how one agent can discover another agent's capabilities, delegate a task to it, receive progress updates, and get the result — all in a standardized way.

Where MCP servers are tools (they do specific things when called), A2A agents are autonomous workers (they receive tasks, plan how to complete them, use their own tools and resources, and report back). An A2A agent has its own reasoning loop, its own tool access, and its own understanding of how to complete work.

A2A defines three key concepts:

Agent Card — a JSON document that describes what an agent can do, what inputs it accepts, and what outputs it produces. Roughly equivalent to an MCP server's server/discover response, but for an autonomous agent rather than a tool server.

Task — the unit of work delegated from one agent to another. A task has inputs, a lifecycle (submitted → working → completed/failed), and outputs. A2A tasks can be long-running; the delegating agent polls for completion or receives a webhook callback.

Message stream — a real-time channel for agents to communicate during task execution: progress updates, clarifying questions, partial results, and status changes.

Examples of what you'd build with A2A:

• An orchestrator agent that breaks a complex research task into subtasks and delegates them to specialized subagents
• A coding agent that delegates deployment verification to a separate infrastructure agent
• A customer support agent that escalates complex cases to a specialized billing agent
• A multi-step workflow where different agents handle different stages of a pipeline

How they work together

In a multi-agent system, MCP and A2A operate at different layers simultaneously.

An orchestrator agent receives a task from a user. It uses A2A to delegate parts of the work to specialized subagents — a research agent, a writing agent, a code agent. Each subagent, in turn, uses MCP to access the tools it needs: the research agent calls an MCP server to search documentation; the code agent calls an MCP server to execute code; the writing agent calls an MCP server to check the company style guide.

The protocols don't overlap. A2A handles the agent-to-agent coordination layer. MCP handles the agent-to-tool access layer. You need both for a complete multi-agent system.

User
  │
  ▼ (natural language request)
Orchestrator Agent
  │
  ├─ A2A → Research Agent ──── MCP → Documentation Server
  │                       └── MCP → Web Search Server
  │
  ├─ A2A → Code Agent ──────── MCP → Code Execution Server
  │                       └── MCP → GitHub Server
  │
  └─ A2A → Writing Agent ───── MCP → Style Guide Server

Governance and ecosystem

MCP is governed by the Agentic AI Foundation (AAIF) — a neutral industry body that took over from Anthropic in 2026. It's supported by every major AI lab and AI editor (Claude, Cursor, Windsurf, VS Code Copilot, Gemini).

A2A is Google's protocol, currently managed by Google DeepMind. It's implemented in Google's agent products and by some third-party frameworks. It's not yet adopted as broadly as MCP across the non-Google ecosystem.

This governance split is relevant if you're making architectural decisions. MCP has broader client support today — any AI editor that supports MCP can use your MCP server. A2A has deeper integration in Google's ecosystem — Gemini, Google Cloud's agent products, and some enterprise workflow platforms.

Which to use for your use case

Build an MCP server if: you want to expose data or capabilities to AI agents — documentation, a database, an API, a file system, a specialized computation. Your server responds to specific tool calls; it doesn't make decisions or manage its own workflow.

Build an A2A agent if: you want to create an autonomous worker that can receive open-ended tasks, reason about how to complete them, use its own tools, and report results. Your agent manages a workflow, not just a single operation.

Build both if: you're building a complete multi-agent system where orchestrators coordinate specialized workers, each of which needs access to external tools and data.

For most developers starting out with agent infrastructure — making a website or API queryable by Claude, Cursor, or other AI editors — MCP is the right starting point. A2A becomes relevant when you need multiple agents coordinating on complex multi-step work.