MCP vs A2A: Model Context Protocol and Agent-to-Agent Communication in AI App Development

Developers building AI applications are encountering two new paradigms that make AI agents more powerful and flexible: Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication. These technologies tackle different challenges in modern LLM app architecture – one focuses on giving a single AI model structured access to external tools and data, while the other enables multiple AI agents to communicate and collaborate. In this post, we’ll explain what MCP and A2A are, how they differ, and how you can leverage both to create smarter AI agents. By the end, you’ll understand why MCP is like a “universal adapter” for AI tools and data, and A2A is like a “common language” for multi-agent systems.

What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open standard that defines how AI models (especially large language models) can receive external context, such as data from databases, files, APIs, or other tools, in a structured, unified way​ (modelcontextprotocol.io). In simpler terms, MCP is a protocol for plugging your AI agent into various information sources and services without custom hacks for each one. Think of MCP like a USB-C port for AI applications – just as USB-C standardizes how devices connect to peripherals, MCP provides a standardized way for AI models to connect to different data sources and tools. This means instead of writing custom integration code every time your AI assistant needs to fetch data from a new source, you use MCP as a universal interface.

MCP was originally introduced by Anthropic (the company behind Claude) and has since been adopted by the broader AI community as a neutral standard ​(dev.to). It addresses a big pain point: historically, every time you wanted an AI model to access some external knowledge or perform an action (like look up a document, query a database, or call an API), you had to wire it up with bespoke code.
Each integration was one-off and fragile, resulting in “fragmented integrations” across different tools. MCP simplifies developers’ lives by replacing those fragmented, custom connectors with one consistent protocol. As one article puts it, “MCP acts as a universal adapter, enabling LLMs to access real-world data and perform actions in a consistent and scalable manner. It promotes AI tools interoperability – your model can use a growing library of pre-built MCP connectors instead of reinventing the wheel for each tool​.

How does MCP work? At a high level, MCP follows a client-server design​. An AI application (the MCP client) can connect to one or more MCP servers. Each MCP server exposes a specific data source or capability through the standard protocol​. For example, one MCP server might expose a company database, another might provide a web search function, and another could connect to an email API. The AI model (through the client) sends a query or request via MCP to the appropriate server, and the server returns the result or performs the action.
All of this communication follows the MCP specification (using standard formats and authentication), so from the AI’s perspective, using a new tool is as straightforward as connecting a new USB device. Developers have already built MCP servers for tools like Google Drive, Slack, GitHub, and databases, so AI agents can “plug in” quickly (anthropic.com​, dev.to). Crucially, MCP is vendor-agnostic – it’s an open protocol, not tied to one AI provider – so you can use it with different LLMs and platforms interchangeably​.

MCP in action: Imagine you’re building a customer support AI assistant. Without MCP, you would have to manually code how the assistant fetches order data from your database, retrieves the latest shipping status from an API, and pulls product information from a knowledge base. With MCP, you could set up standardized connectors for each of these data sources (database, shipping API, knowledge base) and your AI model can query them on the fly with a unified approach. The model might ask the “Orders” MCP server for details of order #12345, instead of you having to hardcode a SQL query – the MCP server handles it and returns structured data. This makes the AI more context-aware and powerful, since it can tap into live information securely and seamlessly. In short, MCP gives a single AI agent a toolbox of capabilities that are easy to add or remove. If you switch your database or add a new data source tomorrow, as long as there’s an MCP server for it, your AI can access it without requiring significant code changes.

What is Agent-to-Agent (A2A) Communication?

While MCP equips one AI agent with tools and data, Agent-to-Agent (A2A) communication involves multiple AI agents communicating with each other. A2A is an open protocol, recently introduced by an industry consortium led by Google, that allows AI agents to communicate, exchange information, and coordinate actions through a standardized messaging format (developers.googleblog.com).
In essence, A2A provides AI agents with a common language or messaging protocol, allowing agents from one vendor or framework to interact with agents from another, much like how one web service can communicate with another via HTTP. Developers have described A2A as “the AI equivalent of HTTP” — a universal protocol that lets any agent talk to any other agent without custom translation layers​ (medium.com).

Why A2A, and what problem does it solve? As AI systems become more sophisticated, there’s a growing trend toward multi-agent systems – instead of one monolithic AI trying to do everything, you have a team of specialized AI agents, each handling a part of the task.
For example, you might have one agent that specializes in planning, another in retrieving information, another in performing calculations, and another in writing the final answer. This specialization can outperform a single generalist model, much like a team of experts can be more effective than a single jack-of-all-trades.
However, until recently, if you wanted these agents to work together, you had to cobble together custom integrations: one agent might call another via an API with a unique format, and you’d need glue code to translate outputs to inputs across agents. It quickly becomes a spaghetti mess of adapters, as one engineer lamented – without a standard, “every connection between agents requires custom code” and adding new agents multiplies complexity​. A2A was created to eliminate that headache by providing a standard way for agents to interact.

Google’s A2A protocol (announced April 2025) emerged from this need for interoperability. It launched with support from over 50 tech partners (including enterprise software companies and AI frameworks like LangChain) to ensure it’s broadly useful​ (developers.googleblog.com). A2A defines how agents should format messages, how to handle dialogues or threads, how to advertise their capabilities to each other, and how to do error handling – so that any A2A-compliant agent can plug into a conversation with another. In practical terms, this means you could have an OpenAI-powered agent, an Anthropic Claude agent, and your own custom Python agent all seamlessly exchange tasks and data if they speak A2A​. They would send each other messages (like JSON requests/responses under the hood, following the protocol spec) rather than, say, one having to be hard-coded as a “tool” of the other.

Think of A2A as giving AI agents a shared communication bus. It allows for a dynamic, modular system of AIs. Using a friendly analogy: A2A is like the social network or a chatroom for AI agents – it lets them “friend” each other and have conversations to collaborate on problems ​(medium.com).
For example, imagine your calendar AI automatically coordinating with your travel AI to reschedule meetings when a flight is delayed.
In an A2A-enabled world, your “Calendar Agent” can send a message to your “Travel Agent” saying, “My flight got delayed, can you find a later connecting flight and update the schedule?” The travel agent understands the request and replies with the new itinerary.
The calendar agent then updates your schedule. All of this can happen through standardized A2A messages, without requiring both agents to be built on the same platform or explicitly coded to communicate with each other in a bespoke way. They simply adhere to the A2A protocol, much like two computers follow the same network protocol to exchange data.

Multi-agent examples: Google has demonstrated an Agent Development Kit (ADK) that utilizes the A2A model to orchestrate multiple agents collaborating on a task. Instead of one LLM doing everything, you might have a team of agents with distinct roles (​gyliu513.medium.com). For example, you could split a complex task as follows:

• PlannerAgent – breaks down the overall task into sub-tasks​.

• ToolAgent – fetches information or processes data as needed (e.g. calls MCP tools or APIs for data).

• WriterAgent – crafts the final response or output based on inputs​.

• ReviewerAgent – evaluates and refines the output for quality​.

Each of these agents is an independent entity, possibly running on different systems or models, and they communicate via A2A messages to coordinate. The Planner might message the ToolAgent saying “I need data on X,” The ToolAgent returns the data, the WriterAgent asks the Planner for clarification on the structure, and so on. Because A2A standardizes the format of these messages (text content, function call requests, etc.), the development focus shifts to the logic of each agent rather than the plumbing of how they talk​.
This modular, message-passing approach makes the whole system more maintainable and extensible. You can add a new agent (say, a TranslatorAgent) later without breaking the others – as long as it speaks A2A, it can join the conversation. In summary, A2A enables multi-agent systems where collaboration is as straightforward as calling a function, except the “function” might be another intelligent agent on the network.

MCP vs A2A: Key Differences and Roles

It’s clear that MCP and A2A serve different purposes in an AI system. To put it succinctly, MCP is about connecting an AI agent to external tools/data in a structured way, whereas A2A is about connecting AI agents in a standardized way. They operate at different layers of your AI app architecture. Here’s a breakdown of their key differences:

• Focus and Scope: MCP is focused on a single agent’s context. It standardizes how one AI model can pull in information or actions from external sources (files, databases, APIs, etc.). A2A is focused on multi-agent interaction – it enables two or more independent AI agents to communicate and coordinate as a team. If MCP is equipping one agent with all the info it needs, A2A is enabling a society of agents to divide and conquer tasks.

• Communication Style: With MCP, the communication is typically between an AI agent and a tool/service (client-to-server). The agent sends a query via MCP and gets back a result. It’s a structured, often transactional exchange (much like a function call: query -> result). In A2A, the communication is agent-to-agent dialog. Agents send each other messages, which could be questions, commands, or data. This is more conversational and dynamic. One agent’s output becomes another agent’s input, and they may have back-and-forth exchanges to reach a goal​.

• Role in System Architecture: MCP acts as the “tool/plugin interface” for an AI model. It ensures the model can access whatever external resources it needs uniformly (hence being called a “universal adapter” for tools​). A2A acts as the “coordination layer” for multi-agent systems – a kind of message bus that any agent can plug into to talk with others. You can think of MCP as extending an agent’s capabilities vertically (connecting it deeper into data sources), whereas A2A extends capabilities horizontally (connecting it across to other agents).

• Analogy (Toolbox vs. Team): Using a builder-friendly analogy, MCP is like giving one master craftsman AI a fully stocked toolbox. Our single AI agent can use its hammer, screwdriver, and drills (various MCP-connected tools and data) as needed to build a solution. In contrast, A2A is like having a team of specialist AI co-workers who speak the same language. Instead of one AI doing everything, you have the planner, the carpenter, the electrician, and the painter all chatting in a common channel to build a house together. One approach isn’t “better” than the other – they’re just different paradigms. A lone genius with great tools (MCP) might solve straightforward tasks efficiently, whereas a coordinated team (A2A) might tackle more complex, multifaceted projects.

• Example Use Cases: If your goal is to build an AI assistant that can retrieve information from various sources and present an answer, you might lean heavily on MCP – e.g. a single Q&A agent that uses MCP to fetch answers from a docs database and a weather API. If your goal is to build an AI system that handles a process end-to-end (say, an AI that plans an event: budgeting, venue booking, scheduling, and invites), you might design it as multiple agents using A2A – one agent handles budget calculations, another searches venues, another schedules, and they pass information via A2A. In many real-world scenarios, you will use both: for instance, each specialized agent in a multi-agent system might use MCP to access its tools (a Research Agent using MCP to query databases, etc.), and then the agents coordinate via A2A messages.

It’s important to note that MCP and A2A are complementary rather than competing. Google’s announcement explicitly states that *“A2A is an open protocol that complements Anthropic’s Model Context Protocol (MCP), which provides helpful tools and context to agents”*​. MCP provides the mechanism for an agent to pull in data and execute actions, while A2A offers the mechanism for agents to share results and delegate tasks among themselves. Used together, they enable an architecture where each agent is both well-informed (thanks to MCP) and well-coordinated (thanks to A2A).

Figure: Example workflows for MCP vs A2A. In the MCP workflow (bottom dashed box), a single AI agent queries two external tools/data sources via a standardized MCP interface and receives results (dashed arrows) it can use. In the A2A workflow (top dashed box), multiple AI agents (A, B, C) communicate by sending messages to each other using the A2A protocol (double-headed arrows), enabling them to collaborate on a task. MCP focuses on connecting an agent to context, whereas A2A focuses on connecting agents.

Using MCP and A2A Together in AI Applications

Now that we’ve differentiated MCP and A2A, you might wonder which paradigm to use for your own AI application. The truth is, you don’t have to choose one or the other – you can and often should use both, depending on what your AI app needs to do. MCP and A2A fit into different layers of an AI app’s architecture, and together they form a powerful stack for building robust AI systems.

If you’re just starting out building an AI-powered app, a practical approach is to begin with a single capable agent and use MCP to give it the external knowledge and abilities it needs. For example, you might prototype a coding assistant that uses MCP to access a GitHub repo, a documentation database, and a Stack Overflow search. This single agent can be quite powerful with the right context plugged in. MCP will make it easier to swap in better data sources or additional tools as your app grows, without requiring you to rewrite your core logic.
It essentially future-proofs your agent’s connectivity by decoupling the what (the data/tool needed) from the how (the integration details). Many developers are excited about MCP because it fosters an ecosystem of reusable connectors – you can build or use an existing MCP server for, say, Slack or Jira, and then any of your AI agents can leverage that connector with minimal effort​.

As your application or ambitions expand, you might find that a multi-agent approach becomes beneficial. That’s where you’d introduce A2A communication. Perhaps your initial single agent is getting overloaded trying to handle too many concerns, or you identify distinct subtasks that could be handled in parallel or by specialized logic. You can then refactor your system into multiple agents that communicate via A2A.
Continuing the example, you might split your coding assistant into a “Planning Agent” that breaks down a coding task, a “Coding Agent” that writes code (using the GitHub MCP connection), and a “Testing Agent” that runs tests. These agents would talk to each other via A2A – the Planning agent might delegate writing to the Coding agent, then ask the Testing agent to verify the output, and so on. By doing this, you’ve made your system more modular and scalable.
Each agent can be developed and improved independently, and as long as they stick to the A2A protocol, they can plug-and-play with each other. In the long term, this modular architecture can save a lot of development time. One case study noted that using a standardized agent communication protocol drastically cut down the “plumbing” code – developers spend less time writing glue and more time on actual logic​.

Another advantage of combining both paradigms is the flexibility and innovation they offer. MCP gives your agents easy access to new capabilities, and A2A gives you the freedom to bring in new agents, such as those provided by third-party services or open-source communities, into your system. Because both MCP and A2A are open standards with broad support, you’re not locked into a single vendor’s ecosystem.
You could use an OpenAI model as one agent, an open-source LLM as another agent, and tools from various providers, all in one coherent system. This interoperability is by design: A2A was developed with the vision of agents from different vendors interoperating seamlessly​, and MCP was designed to be an open “USB-C” standard usable by anyone​. As a builder, this means you can mix and match the best components for the job at hand.

Finally, consider the complexity of your problem when deciding how to architect it. If a problem domain is well-bounded and can be handled by one agent just pulling in some extra data (for example, “answer questions about our company’s policies” can be mostly solved by one QA agent that uses MCP to read policy documents), then a single-agent-with-MCP solution might suffice.
If a problem naturally breaks into stages or requires different expertise (for example, “analyze this data, generate a report, and then draft emails to stakeholders about the findings” – which entails data analysis, report writing, and communication steps), an A2A multi-agent design makes a lot of sense, possibly with MCP in each stage for specific tools.
You might start with one approach and evolve to the other as you scale. The good news is that since MCP and A2A complement each other, you can incrementally add one to an existing system using the other. Your single MCP-augmented agent can later become the coordinator agent in an A2A system, or vice versa, your multi-agent system can have each agent enhanced with MCP access to relevant data.

Conclusion: Empowering AI Agents with the Right Paradigms

The emergence of Model Context Protocol and Agent-to-Agent communication is an exciting development for AI builders. These paradigms are unlocking new levels of capability in AI agents by addressing two key aspects of the AI app equation: knowledge and tools, as well as collaboration. MCP ensures an AI model is not working in a vacuum – it can be fed rich context and perform actions in the real world through a standardized interface.
A2A ensures an AI agent is not working alone – it can coordinate with other agents in a common language to tackle complex tasks together. Whether you choose to equip one AI agent with a world of tools (MCP) or orchestrate a team of AI agents in concert (A2A) – or both – will depend on your project’s needs. Often, the most powerful solutions will combine these approaches, allowing each agent in a multi-agent system to be both well-equipped and well-coordinated.

In all cases, adopting these open protocols can save you time and effort. Developers are no longer spending 80% of their time building ad-hoc plumbing between models and services – MCP and A2A provide robust highways for data and messages​. This lets you focus on the creative part of building AI applications, rather than the boilerplate.
We encourage you, as a builder, to explore both MCP and A2A as you design your next AI app architecture. Try adding an MCP integration to give your AI new powers, or spin up a second agent and use A2A to have it collaborate with the first. You may discover that many problems become easier to solve when your AI agents can both pull in the right context and work in tandem with others.

The field of AI is moving fast, and these paradigms ensure you can keep up by composing systems in a scalable way. Much like interchangeable parts and communication protocols revolutionized traditional software, MCP and A2A are poised to revolutionize how we build AI systems – making them more modular, interoperable, and powerful. It’s a thrilling time to innovate. So plug in that context, open up those communication channels, and see what your AI agents can achieve. With MCP and A2A in your toolkit, the possibilities for LLM app architecture and multi-agent systems are vast. Happy building!

References:

1. Anthropic, Introducing the Model Context Protocol (MCP) – Anthropic News (Nov 2024)​anthropic.com​anthropic.com.

2. ModelContextProtocol.io, MCP Introduction – Official MCP Docs​modelcontextprotocol.io.

3. P. Belagatti, Model Context Protocol (MCP): 8 MCP Servers Every Developer Should Try! – Dev.to (Apr 2025)​dev.to​.

4. Google Developers Blog, Announcing the Agent2Agent (A2A) Protocol (Apr 2025)​developers.googleblog.com​.

5. M. Desai, Meet Google A2A: The Protocol That Will Revolutionize Multi-Agent AI Systems – Medium (Apr 2025)​medium.com.

6. M. Desai, The Power Duo: How A2A + MCP Let You Build Practical AI Systems Today – Medium (Apr 2025)​medium.com​.

7. G. Liu, Building Modular AI Agents with Google ADK and MCP – Medium (Apr 2025)​gyliu513.medium.com.