How do A2A and MCP relate? Overlap or separate paths for Agent-to-Agent communication?

[michelschep]

This week I watched the MCP Dev Days session (Microsoft Reactor event), where an agent-to-agent solution was also presented, but using the Model Context Protocol (MCP) instead of A2A.
Link to session: https://www.youtube.com/watch?v=ovB8FZ7LAAk&t=105s

This raises some questions:

-How do A2A and MCP relate in the context of agent-to-agent communication?
-Are both communities (A2A and MCP) working on solving the same problem?
-Do I need both frameworks, or are they merging at some point?
-Or is this more of a “friendly competition” between two different approaches?

Any clarification would be appreciated. I’d love to better understand how these efforts align or diverge so I can make informed architectural decisions.

Thanks!

[lukehinds]

I'll have a go at explaining this, though others might nail it better! I likely will upset someone for getting something wrong, but hey, that's internet specficiations for you, they bring about quite a bit of passion and things get flustered now and then.

We started with a concept called 'Tools' (or 'Function calling' in OpenAI parlance). The LLM is presented with tools it can pick and use from - at its own disposal. The Tools have defined schemas - for example a name it can use when it wants a call the Tool search_internet, parameter specifications, expected return types / structured output, and other metadata that tells the model how to properly invoke them. The LLM decides then to use these Tools in a natural language kind of way.

User asks: "What is the price of bitcoin today"

LLM reasons: "Hmmm, I should look this up on the internet. Oh, I have the search_internet tool I can use! Let me call that"

This sparked a gold rush of tool-building across projects. Tools became (and arguably still are?) one of a few first 'Killer Apps' in AI - at least until the next shiny thing comes along. We all had that 'holy shit, it's alive!' moment watching LLMs autonomously write code, search the web, and conduct research. We gave them Tools, and Tools are what it's all about, evolution loves Tools! - Tools are what took us from being dumb Apes, to bald homicidal Tool wielding Apes (which is why we should not give LLMs sharp objects).

But here's where it got messy: no standardisation. Everyone was reinventing the wheel, with Tools tightly coupled to specific codebases. Very little reusability and agreed approaches to communication protocols, auth etc.

Enter Anthropic's MCP - a standardisation effort for Tools. It defines transport protocols (initially stdio and SSE, now streaming HTTP), authentication patterns, and message formats. This standardisation is what made MCP take off. Suddenly, "Tools" became reusable, plug-and-play components - like USB for AI. So while MCP is getting all the glory, hanging out with the stars and being interviewed on the red carpet, it's really just "Tools" that learned to play nicely with others and follow some playground rules.

Now, A2A tackles a different beast. Beyond individual tool usage, we need standards for how autonomous agents communicate with each other. How do they discover peers? Advertise capabilities? Handle async operations? A2A addresses these inter-agent concerns: service discovery, capability negotiation, streaming bidirectional communication, webhook patterns for long-running tasks, and message routing - none of which MCP covers.

TL;DR:

MCP standardizes how agents use tools (making them shareable and reusable).

A2A standardizes how agents talk to each other.

MCP is about agent-to-tool interaction; A2A is about agent-to-agent collaboration.

[michelschep]

Thx for the elaborated answer. Will read it later more carefully.
I was confused by this blog post: https://devblogs.microsoft.com/blog/can-you-build-agent2agent-communication-on-mcp-yes
There is explained that mcp can do the a2a things.

[lukehinds]

This is where things start to feel somewhat superfluous. At their core, we're really just talking about collections of APIs, network protocols, persistence mechanisms, queues, caching layers, and so on - prior art grouped together and labeled as "Agentic". But in essence, they remain what they are: APIs with supporting infrastructure to manage state, persistence, communication, etc to parly back and forth with the real meat of the bones magic maker, the large language model.

Given that, sure there's no inherent reason MCP couldn't do what A2A does - or vice versa - provided someone is willing to build the necessary boilerplate. Ultimately, the little boundary there is between these systems comes down to implementation detail and abstraction, not any fundamental difference in capability. This is why I always tend to be of a, 'show me the working code' person, more than what's in spec.

[michelschep]

That's a good one. "Show me the code" helps a lot. I use to to start some pet project and start with a use case (always good to have a functional goal) and then try to achieve what I want.
I could imagine that we have several agent "levels" (requiring less or more agentic properties) and that each level can achieve its goal by using maybe only MCP (when the agent is actually only a tool) or when it gets more complicated (more reasoning and long running) with A2A.

[ognis1205]

I consider myself fairly familiar with the fundamentals of MCP, although I may not be fully up to date on its latest usage patterns — so please take the following as a personal perspective rather than an authoritative view. The same applies to A2A; I’m sharing my personal understanding as someone closely following the developments.

That said, I’d like to offer my thoughts on how both protocols — MCP and A2A — approach agent-to-agent communication, especially with regard to their design philosophies and long-term implications.

Both A2A and MCP Can Enable Agent-to-Agent Communication

I’ve watched the session you shared, and I agree that wrapping an agentic system as an MCP server can indeed support agent-to-agent interaction. However, I believe there are important differences in how each protocol approaches this — and those differences shape their potential trajectories.

Differences in Agent-to-Agent Communication Models: MCP vs A2A

MCP was originally designed to connect AI assistants (agents) to existing tools and systems — not specifically for agent-to-agent interaction. So when extending MCP to multi-agent scenarios, agents must be treated as MCP tools. In this setup, the MCP server presents an input schema to the client agent, who is then responsible for generating inputs conforming to that schema.

This reveals a fundamental contrast between MCP and A2A.

• MCP focuses on mechanistic interoperability — aiming to standardize how tools (or agents) expose their capabilities structurally, using shared input/output schemas so that an LLM can reason about tool invocation in a uniform way.
• A2A, by contrast, aims for semantic interoperability — focusing on aligning what agents intend, what capabilities they offer, and why they act, enabling richer multi-agent coordination beyond structured tool calls.

If MCP is to scale horizontally as a basis for agent-to-agent communication, it would eventually need to address semantic agreement over input/output schemas. As of now, that is not a primary focus of MCP. A2A, however, tackles this from the beginning, using concepts like AgentCard, AgentSkill, and declarative capability discovery to establish a shared semantic substrate.

My View: A2A Frames the Problem More Directly

In the long run, of course, the effectiveness of either approach will depend on how capable agents become. If future agents can reason well enough about foreign schemas and contexts, semantic alignment may emerge as a side-effect of scale. But if we assume that agents will increasingly be asked to perform open-ended tasks under trust boundaries, then I believe the A2A framing — with its emphasis on meaning, delegation, and intent — provides a more direct foundation for agent-to-agent coordination.

[ognis1205]

P.S.,

In this light, I believe what is increasingly required of us — as software developers — is to shift our attention toward the open-endedness of agents. Unlike traditional software components that operate within well-defined APIs and deterministic flows, agents inhabit a space of ambiguity, intent, and negotiation.

This ambiguity is not a limitation, but rather where much of the potential and expressive power of AI agents lies. It also introduces new challenges: reasoning under uncertainty, coordinating across evolving goals, and operating across trust boundaries. This is precisely where A2A becomes both difficult and fascinating — offering a protocol and abstraction layer for building meaning-aware agent systems that don’t simply execute, but adapt, delegate, and align.

[michelschep]

Thx for your answer. It helps a lot in understanding and reasoning about all the (new) concepts.
There is a lot going on (protocols are still in development, implementation are already building build, everybody has an opinion, and concepts like agents are quite abstract to it is sometimes difficult to understand what everybody means and thinks.

[sing1ee]

Some Thoughts on A2A and MCP

I’ve developed several MCP servers myself and have read a lot of articles about A2A. Overall, I think A2A is a better design.

From architecture to implementation, A2A is more robust and complete than MCP. However, right now there are many available MCP servers, while A2A doesn’t yet have a working agent — this is really a matter of timing. As a result, the MCP ecosystem is much larger than A2A’s.

From a code perspective, both are still in the early stages, so they’re relatively simple and can be easily modified. That means it’s hard for either to have an overwhelming advantage in terms of design.

There’s another challenge for A2A: whether the logic of a general-purpose agent, like Manus, actually holds up. If general-purpose agents do work, then MCP is already sufficient.

Recently, I’ve been looking into Claude code’s subagents. This seems like a great use case for an A2A agent, but Claude’s implementation takes a general-purpose agent approach — all it needs is a custom system prompt and an independent runtime environment. There’s no need for a truly independent agent. One reason for this is that everything shares the same codebase context.

Starting from chatbots, the hottest use case right now is AI coding. What’s next? Will it be multi-agent applications?

[luyiourwong]

The A2A team has actually published an article on this exact topic:
https://a2a-protocol.org/latest/topics/a2a-and-mcp/

That said, in practice MCP’s spec is even more bare-bones than how it’s described there — it basically just requires a JSON with a name and args. Because it’s so minimal, you can very easily extend MCP to handle agent-to-agent communication, including the things A2A calls its advantages (like context passing, state sync, etc.).

In my own AI service, I’ve implemented one-to-one agent communication purely over MCP, and for internal environments that’s already more than enough. A2A’s real edge is when two agents with no pre-negotiated contract need to talk.

Personally, I wish A2A was built as a layer on top of MCP instead of being its own separate thing. Right now, almost every client supports MCP, but you’ll have a hard time finding a client that supports A2A — which makes its position a bit awkward.

[HV9903]

to answer your question @michelschep here is what i know.
Hey @lukehinds thats a great explaination of how tool calling has changed from fixed hardcoded to dynamic discovery. And i agree with most of the part but there is still doubt in my mind.
From what i understood by reading and implementing mcp is that this protocol is for standardization of tool calling so that once server is built host can integrate it by creating a client instance without need to implement it again from scrach. Now it has moved to integration from implementation. we can call it vertical capability upgrade.

While if we look at a2a it is a horizontal capability upgrade by utilizing other agents capabilities instead of doing all in same agent.

From my point of view it is possible to use another agent as a client and one agent as server then apply mcp to allow collaboration, or should i say one sided work assignment instead of collaboration. In A2A it is proper collaboration as both agents can use each others capability not like in case of mcp.(Note: i have not implemented a2a so i only know about a2a through official google a2a specification page) .