In the rapidly evolving landscape of AI, protocols like Google's Agent-to-Agent (A2A) and Anthropic's Model Context Protocol (MCP) are emerging as key enablers for building sophisticated AI systems. Understanding the differences between these protocols is crucial for making informed decisions about which one best suits your project's needs. This post will break down the core distinctions between A2A and MCP to help you choose the right tool for the job.

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The Core Difference: Partnership vs. Capability Usage

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The most succinct way to differentiate the two protocols is this:

• A2A is about agents partnering on tasks. It's designed for scenarios where multiple AI agents need to collaborate, delegate tasks, and exchange information to achieve a common goal.
• MCP is about agents using capabilities. It focuses on how agents access and interact with external tools, APIs, and data sources.

Think of A2A as the protocol for orchestrating a team of AI agents, while MCP provides each agent with the tools they need to perform their individual roles.

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Stateful Interactions and Long-Running Tasks

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One of the most significant differentiators is that A2A is intentionally stateful. This means it's designed to manage tasks that can be long-running, complex, and involve multiple steps. A2A provides mechanisms for tracking the progress of these tasks, handling interruptions, and resuming them later. In essence, A2A introduces a Task object with defined states ("submitted", "working", "input-required", etc.) which allows client and server to have a single, agreed upon understanding of state over time. MCP, on the other hand, doesn't explicitly manage task state.

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Agent Discovery and the "App Store Moment"

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A2A facilitates agent discovery through the use of an "Agent Card," a JSON metadata document that describes an agent's capabilities, skills, and contact information. This allows agents to dynamically find and interact with other agents in the ecosystem. A2A Servers MUST make an Agent Card available and clients are able to access them via:

• Well-Known URI: Accessing a predefined path on the agent's domain (see Section 5.3).
• Registries/Catalogs: Querying curated catalogs or registries of agents (which might be enterprise-specific, public, or domain-specific).
• Direct Configuration: Clients may be pre-configured with the Agent Card URL or the card content itself.

Google's hope is that A2A will create an "app store moment" for AI developers, providing a standardized way to expose their enterprise AI capabilities to a wider audience. This is a move toward "tasks" as a unit of capability, as opposed to MCP's more granular, tool-based approach.

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Interoperability and Standard Interfaces

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A key goal of A2A is to create interoperability between different agent frameworks. By providing a standard interface at the agent level, A2A aims to allow you to use agentic capabilities regardless of the underlying framework or implementation. A2A’s stated goals are:

• Interoperability: Bridge the communication gap between disparate agentic systems.
• Collaboration: Enable agents to delegate tasks, exchange context, and work together on complex user requests.
• Discovery: Allow agents to dynamically find and understand the capabilities of other agents.
• Flexibility: Support various interaction modes including synchronous request/response, streaming for real-time updates, and asynchronous push notifications for long-running tasks.
• Security: Facilitate secure communication patterns suitable for enterprise environments, relying on standard web security practices.
• Asynchronicity: Natively support long-running tasks and interactions that may involve human-in-the-loop scenarios.

This means that you could potentially use agentic capabilities provided with or without MCP, making MCP optional. MCP is about the connection between LLMs and the tools they are using.

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A2A Strengths

• Managing agent ecosystems: A2A helps manage enterprise "agent zoos" in a way that goes beyond each person spinning up MCP servers (not feasible for non-technical users).
• Orchestrating complex workflows: Ideal for scenarios where multiple agents need to collaborate on a task, exchanging context and delegating responsibilities.
• Building task-oriented AI applications: Aims to simplify the development of AI applications that focus on delivering specific tasks or services.

MCP Strengths

• Seamless integration with tools and APIs: MCP provides a structured way for AI models to access and utilize external resources.
• Standardized tool descriptions: MCP defines a standard format for describing tool capabilities, making it easier for AI models to discover and use them.
• Building AI-powered applications: Enables developers to build AI-powered applications by seamlessly integrating language models with external data sources and tools.
• User control Robust security considerations that take into account user privacy and control over data and operations.

Is A2A just a fancy MCP client?

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It's a fair question. It could be argued that a well-implemented MCP client could achieve similar results to A2A. There is some question of A2A’s value proposition over a well-architected MCP system. However, A2A provides a higher-level abstraction that focuses on tasks and agent collaboration, while MCP is more concerned with the granular details of tool usage.

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Conclusion: Choosing the Right Protocol

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Ultimately, the choice between A2A and MCP depends on your specific use case.

• If you're building a system that requires multiple AI agents to work together on complex, stateful tasks, A2A is likely the better choice.
• If you're focused on enabling AI models to access and utilize external tools and APIs, MCP is the more appropriate option.

In many cases, the two protocols can be used together. An A2A client agent might request an A2A server agent to perform a task. The server agent, in turn, might use MCP to interact with several underlying tools, APIs, or data sources to gather information or perform actions necessary to fulfill the A2A task.

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It will be interesting to see how these protocols evolve and whether A2A can deliver on its promise of creating a vibrant ecosystem of interoperable AI agents.

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