The Mannequin Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a big business consideration over the previous yr. MCP first grabbed the world’s consideration in dramatic style when it was printed by Anthropic in November 2024, garnering tens of 1000’s of stars on GitHub inside the first month. Organizations shortly noticed the worth of MCP as a strategy to summary APIs into pure language, permitting LLMs to simply interpret and use them as instruments. In April 2025, Google launched A2A, offering a brand new protocol that permits brokers to find one another’s capabilities, enabling the fast progress and scaling of agentic methods.
Each protocols are aligned with the Linux Basis and are designed for agentic methods, however their adoption curves have differed considerably. MCP has seen fast adoption, whereas A2A’s progress has been extra of a sluggish burn. This has led to business commentary suggesting that A2A is quietly fading into the background, with many individuals believing that MCP has emerged because the de-facto normal for agentic methods.
How do these two protocols examine? Is there actually an epic battle underway between MCP and A2A? Is that this going to be Blu-ray vs. HD-DVD, or VHS vs. Betamax over again? Effectively, not precisely. The fact is that whereas there may be some overlap, they function at completely different ranges of the agentic stack and are each extremely related.
MCP is designed as a manner for LLMs to know what exterior instruments can be found to it. Earlier than MCP, these instruments had been uncovered primarily via APIs. Nevertheless, uncooked API dealing with by an LLM is clumsy and tough to scale. LLMs are designed to function on the planet of pure language, the place they interpret a process and establish the proper instrument able to conducting it. APIs additionally endure from points associated to standardization and versioning. For instance, if an API undergoes a model replace, how would the LLM find out about it and use it appropriately, particularly when attempting to scale throughout 1000’s of APIs? This shortly turns into a show-stopper. These had been exactly the issues that MCP was designed to unravel.
Architecturally, MCP works effectively—that’s, till a sure level. Because the variety of instruments on an MCP server grows, the instrument descriptions and manifest despatched to the LLM can grow to be huge, shortly consuming the immediate’s total context window. This impacts even the biggest LLMs, together with these supporting lots of of 1000’s of tokens. At scale, this turns into a elementary constraint. Not too long ago, there have been spectacular strides in lowering the token depend utilized by MCP servers, however even then, the scalability limits of MCP are prone to stay.
That is the place A2A is available in. A2A doesn’t function on the stage of instruments or instrument descriptions, and it doesn’t get entangled within the particulars of API abstraction. As a substitute, A2A introduces the idea of Agent Playing cards, that are high-level descriptors that seize the general capabilities of an agent, fairly than explicitly itemizing the instruments or detailed abilities the agent can entry. Moreover, A2A works solely between brokers, which means it doesn’t have the power to work together straight with instruments or finish methods the way in which MCP does.
So, which one must you use? Which one is healthier? Finally, the reply is each.
If you’re constructing a easy agentic system with a single supervisory agent and a wide range of instruments it will possibly entry, MCP alone may be an excellent match—so long as the immediate stays compact sufficient to suit inside the LLM’s context window (which incorporates your entire immediate funds, together with instrument schemas, system directions, dialog state, retrieved paperwork, and extra). Nevertheless, if you’re deploying a multi-agent system, you’ll very doubtless want so as to add A2A into the combo.
Think about a supervisory agent liable for dealing with a request corresponding to, “analyze Wi-Fi roaming issues and suggest mitigation methods.” Somewhat than exposing each potential instrument straight, the supervisor makes use of A2A to find specialised brokers—corresponding to an RF evaluation agent, a person authentication agent, and a community efficiency agent—based mostly on their high-level Agent Playing cards. As soon as the suitable agent is chosen, that agent can then use MCP to find and invoke the particular instruments it wants. On this stream, A2A offers scalable agent-level routing, whereas MCP offers exact, tool-level execution.
The important thing level is that A2A can—and infrequently ought to—be utilized in live performance with MCP. This isn’t an MCP versus A2A determination; it’s an architectural one, the place each protocols may be leveraged because the system grows and evolves.
The psychological mannequin I like to make use of comes from the world of networking. Within the early days of pc networking, networks had been small and self-contained, the place a single Layer-2 area (the information hyperlink layer) was ample. As networks grew and have become interconnected, the bounds of Layer-2 had been shortly reached, necessitating the introduction of routers and routing protocols—often known as Layer-3 (the community layer). Routers perform as boundaries for Layer-2 networks, permitting them to be interconnected whereas additionally stopping broadcast visitors from flooding your entire system. On the router, networks are described in higher-level, summarized phrases, fairly than exposing all of the underlying element. For a pc to speak outdoors of its fast Layer-2 community, it should first uncover the closest router, figuring out that its meant vacation spot exists someplace past that boundary.
This maps carefully to the connection between MCP and A2A. MCP is analogous to a Layer-2 community: it offers detailed visibility and direct entry, nevertheless it doesn’t scale indefinitely. A2A is analogous to the Layer-3 routing boundary, which aggregates higher-level details about capabilities and offers a gateway to the remainder of the agentic community.
The comparability will not be an ideal match, nevertheless it presents an intuitive psychological mannequin that resonates with those that have a networking background. Simply as fashionable networks are constructed on each Layer-2 and Layer-3, agentic AI methods will finally require the complete stack as effectively. On this gentle, MCP and A2A shouldn’t be considered competing requirements. In time, they’ll doubtless each grow to be vital layers of the bigger agentic stack as we construct more and more refined AI methods.
The groups that acknowledge this early would be the ones that efficiently scale their agentic methods into sturdy, production-grade architectures.
