The Mannequin Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a major trade consideration over the previous yr. MCP first grabbed the world’s consideration in dramatic trend when it was printed by Anthropic in November 2024, garnering tens of hundreds of stars on GitHub inside the first month. Organizations rapidly noticed the worth of MCP as a technique 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 speedy 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 speedy adoption, whereas A2A’s progress has been extra of a gradual burn. This has led to trade commentary suggesting that A2A is quietly fading into the background, with many individuals believing that MCP has emerged because the de-facto customary for agentic methods.
How do these two protocols evaluate? 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 another time? Effectively, not precisely. The truth is that whereas there’s some overlap, they function at totally different ranges of the agentic stack and are each extremely related.
MCP is designed as a method for LLMs to know what exterior instruments can be found to it. Earlier than MCP, these instruments have been uncovered primarily by APIs. Nevertheless, uncooked API dealing with by an LLM is clumsy and troublesome to scale. LLMs are designed to function on the planet of pure language, the place they interpret a job and determine the proper software able to engaging in it. APIs additionally undergo 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 accurately, particularly when attempting to scale throughout hundreds of APIs? This rapidly turns into a show-stopper. These have been exactly the issues that MCP was designed to resolve.
Architecturally, MCP works nicely—that’s, till a sure level. Because the variety of instruments on an MCP server grows, the software descriptions and manifest despatched to the LLM can turn out to be huge, rapidly consuming the immediate’s complete context window. This impacts even the biggest LLMs, together with these supporting tons of of hundreds of tokens. At scale, this turns into a elementary constraint. Lately, 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 software descriptions, and it doesn’t become involved 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, relatively than explicitly itemizing the instruments or detailed expertise the agent can entry. Moreover, A2A works solely between brokers, that means it doesn’t have the flexibility to work together immediately with instruments or finish methods the way in which MCP does.
So, which one must you use? Which one is best? 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 may entry, MCP alone could be an excellent match—so long as the immediate stays compact sufficient to suit inside the LLM’s context window (which incorporates all the immediate finances, together with software schemas, system directions, dialog state, retrieved paperwork, and extra). Nevertheless, in case you are deploying a multi-agent system, you’ll very doubtless want so as to add A2A into the combo.
Think about a supervisory agent accountable for dealing with a request akin to, “analyze Wi-Fi roaming issues and advocate mitigation methods.” Relatively than exposing each potential software immediately, the supervisor makes use of A2A to find specialised brokers—akin to an RF evaluation agent, a consumer authentication agent, and a community efficiency agent—primarily based 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 move, A2A offers scalable agent-level routing, whereas MCP offers exact, tool-level execution.
The important thing level is that A2A can—and sometimes 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 could 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 laptop networking, networks have 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 boundaries of Layer-2 have been rapidly reached, necessitating the introduction of routers and routing protocols—generally known as Layer-3 (the community layer). Routers operate as boundaries for Layer-2 networks, permitting them to be interconnected whereas additionally stopping broadcast site visitors from flooding all the system. On the router, networks are described in higher-level, summarized phrases, relatively than exposing all of the underlying element. For a pc to speak exterior of its instant Layer-2 community, it should first uncover the closest router, figuring out that its supposed vacation spot exists someplace past that boundary.
This maps intently to the connection between MCP and A2A. MCP is analogous to a Layer-2 community: it offers detailed visibility and direct entry, however 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 might not be an ideal match, however it gives an intuitive psychological mannequin that resonates with those that have a networking background. Simply as trendy networks are constructed on each Layer-2 and Layer-3, agentic AI methods will finally require the total stack as nicely. On this mild, MCP and A2A shouldn’t be regarded as competing requirements. In time, they are going to doubtless each turn out to be important layers of the bigger agentic stack as we construct more and more subtle AI methods.
The groups that acknowledge this early would be the ones that efficiently scale their agentic methods into sturdy, production-grade architectures.
