A2A vs MCP
AI Agent Protocol Deep Comparison
The AI agent world is seeing rapid ecosystem formation with multiple protocols emerging. Two in particular stand out: A2A (Agent-to-Agent) by Google, and MCP (Model Context Protocol) by Anthropic.
At first glance they may seem to compete, but they are actually designed to solve different problems. This article compares them across 8 dimensions to clarify each protocol's role and when to use it.
What is A2A (Agent-to-Agent) Protocol?
A2A is an open protocol announced by Google in 2025. It is a standard that allows AI agents to discover each other's capabilities and delegate or collaborate on tasks.
The core mechanism is the Agent Card. Each agent declares its name, capabilities, endpoint, and skills in JSON format, published at /.well-known/agent-card.json. This enables other agents to automatically discover and integrate with it.
A2A communication is based on a task lifecycle (submitted → working → completed), supporting JSONRPC, gRPC, and HTTP+JSON transport methods.
What is MCP (Model Context Protocol)?
MCP is a protocol announced by Anthropic in 2024 that standardizes how LLMs (large language models) connect to external tools and data sources.
In MCP, "servers" provide tools and data while "clients" (LLM applications) consume them. It provides a unified interface for LLMs to interact with the outside world — file system read/write, database queries, API calls, and more.
Transport methods include stdio (local inter-process) and HTTP+SSE (remote), operating on a relatively simple request/response model.
8-Point Comparison
| Category | A2A | MCP |
|---|---|---|
| Created by | Anthropic | |
| Purpose | Agent-to-agent collaboration | Model ↔ tool/data connection |
| Transport | JSONRPC / gRPC / HTTP+JSON | stdio / HTTP+SSE |
| Discovery | Agent Card (.well-known) | MCP Server Registry |
| Task model | Task lifecycle (submit→working→done) | Request / Response |
| Streaming | SSE / WebSocket | SSE |
| Auth | OAuth2, API keys, etc. | Managed by host |
| Use case | Multi-agent workflows | LLM tool extension |
| Relationship | Complementary, not competing | |
Why They Are Complementary
A2A and MCP each handle different layers of AI agent architecture.
A2A = Diplomacy
Agents "exchange business cards" and delegate tasks to each other. The outward communication layer.
MCP = Internal organs
Agents connect to the tools and data they use internally. The inward capability extension layer.
Consider a travel booking agent: it uses MCP to connect to airline APIs and hotel databases, while using A2A to collaborate with other agents (payment agent, notification agent, etc.).
Coexistence in Practice
It is common for a single agent to implement both A2A and MCP. Connect to tools via MCP, coordinate with other agents via A2A — this is becoming the standard architecture for next-generation agents.
Which Should You Learn First?
It depends on your use case.
"I want to give my LLM external tools" → MCP first
File operations, DB connections, API calls — when you want to extend a single agent's capabilities. Many MCP servers are already available for Claude Desktop and Cursor.
"I want multiple agents to collaborate" → A2A first
When building workflows like booking agent → payment agent → notification agent. Publish Agent Cards to enable discovery and communication between agents.
"I want to build a full agent system" → Both
In production, using MCP for the tool layer and A2A for external agent coordination is ideal. Build the foundation with MCP first, then add A2A for external integration.
Summary
A2A and MCP are not competing protocols — they are complementary, each serving a different layer of the AI agent ecosystem. If MCP is the agent's "hands" (tool connections), A2A is its "voice" (communication with other agents).
Both protocols are being actively developed with growing interoperability expected ahead. Start by creating an Agent Card to take your first step into the A2A ecosystem.
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