Two protocols are shaping the future of AI integration: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent Protocol (A2A). They solve different problems and work at different layers of the stack. Here is how they compare.
MCP connects an AI model to external tools and data. Think of it as the protocol that lets Claude read your files, query your database, or call your APIs. It is the "hands" of the AI -- how it interacts with the world.
Analogy: MCP is like USB -- a universal connector between a computer and its peripherals (keyboard, mouse, storage, etc.).
A2A enables AI agents to discover, communicate, and collaborate with each other. Think of it as the protocol for multi-agent systems where a "manager" agent delegates tasks to "specialist" agents.
Analogy: A2A is like email/Slack -- a communication protocol between people (or agents) who need to coordinate work.
Yes, and they are designed to be complementary. MCP and A2A operate at different layers of the AI integration stack. A realistic production architecture might use both:
USER REQUEST
|
[Agent A] -- MCP --> [Database Server] (reads data)
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| -- A2A --> [Agent B] (delegates analysis)
| |
| | -- MCP --> [Python Runtime] (runs computation)
| |
| [Result]
|
[Final Response to User]
Each agent uses MCP to connect "downward" to its tools and data sources. Agent A might use MCP to access databases, while Agent B uses MCP to run Python code. MCP provides the "arms and legs" for each agent.
Agents use A2A to communicate "sideways" with each other. Agent A delegates a sub-task to Agent B using A2A. The agents negotiate capabilities, exchange messages, and return results. A2A provides the "communication network" between agents.
MCP is transparent by design. Servers expose their exact tools, schemas, and capabilities to the client. The AI model sees exactly what tools are available and how to call them. A2A, by contrast, supports opaque agents. An agent can advertise what tasks it can handle without revealing how it works internally. This is important for commercial multi-agent systems where agents are provided by different vendors.
MCP servers are manually configured in client configuration files. There is no built-in discovery protocol. You need to know the server exists and how to install it. A2A includes an Agent Card system where agents publish their capabilities at a well-known URL (/.well-known/agent.json). This enables automated agent discovery and marketplaces.
MCP tools are synchronous function calls. You call a tool and get a result. There is no built-in concept of long-running tasks, progress tracking, or task lifecycle. A2A has a rich task model with states (submitted, working, input-required, completed, failed), progress notifications, and the ability for tasks to produce multiple artifacts over time.
MCP has a significant head start. Launched in late 2024, it has hundreds of community and official servers, support from every major AI client (Claude Desktop, Cursor, Windsurf, VS Code), and a thriving ecosystem. A2A, released in early 2025, is still in its early stages with a smaller but growing ecosystem, primarily centered around Google Cloud and its partners.
MCP is for tool integration. If you want to give an AI model the ability to use tools, read data, and interact with systems, MCP is the established standard with the largest ecosystem.
A2A is for agent orchestration. If you are building multi-agent systems where different AI agents need to discover each other and collaborate on complex tasks, A2A provides the communication layer.
For most developers today, starting with MCP is the pragmatic choice. The ecosystem is mature, the tooling is excellent, and it solves the most immediate need: giving AI models access to your tools and data. As multi-agent architectures become more common, A2A will become increasingly relevant, and the two protocols will work together.