The Mannequin Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a big trade consideration over the previous yr. MCP first grabbed the world’s consideration in dramatic trend when it was revealed by Anthropic in November 2024, garnering tens of 1000’s of stars on GitHub throughout the first month. Organizations shortly noticed the worth of MCP as a approach 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 enables brokers to find one another’s capabilities, enabling the fast progress and scaling of agentic programs.
Each protocols are aligned with the Linux Basis and are designed for agentic programs, however their adoption curves have differed considerably. MCP has seen fast 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 normal for agentic programs.
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 over again? Effectively, not precisely. The fact is that whereas there may be some overlap, they function at totally different ranges of the agentic stack and are each extremely related.
MCP is designed as a approach 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 tough to scale. LLMs are designed to function on the planet of pure language, the place they interpret a activity and determine the fitting software 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 have been exactly the issues that MCP was designed to unravel.
Architecturally, MCP works properly—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, shortly consuming the immediate’s complete context window. This impacts even the biggest LLMs, together with these supporting a whole lot of 1000’s of tokens. At scale, this turns into a elementary constraint. Lately, there have been spectacular strides in decreasing 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 an alternative, A2A introduces the idea of Agent Playing cards, that are high-level descriptors that seize the general capabilities of an agent, reasonably than explicitly itemizing the instruments or detailed abilities the agent can entry. Moreover, A2A works completely between brokers, that means it doesn’t have the power to work together straight with instruments or finish programs the way in which MCP does.
So, which one do you have to use? Which one is best? Finally, the reply is each.
In case you are constructing a easy agentic system with a single supervisory agent and quite a lot of instruments it could actually entry, MCP alone might be a super match—so long as the immediate stays compact sufficient to suit throughout the LLM’s context window (which incorporates your complete immediate price range, 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 seemingly want so as to add A2A into the combination.
Think about a supervisory agent answerable for dealing with a request resembling, “analyze Wi-Fi roaming issues and advocate mitigation methods.” Somewhat than exposing each attainable software straight, the supervisor makes use of A2A to find specialised brokers—resembling an RF evaluation agent, a person 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 circulate, 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 resolution; it’s an architectural one, the place each protocols might 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 adequate. As networks grew and have become interconnected, the bounds of Layer-2 have been shortly reached, necessitating the introduction of routers and routing protocols—referred to as Layer-3 (the community layer). Routers perform as boundaries for Layer-2 networks, permitting them to be interconnected whereas additionally stopping broadcast site visitors from flooding your complete system. On the router, networks are described in higher-level, summarized phrases, reasonably than exposing all of the underlying element. For a pc to speak outdoors of its rapid 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, 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 will 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 programs will ultimately require the complete stack as properly. On this gentle, MCP and A2A shouldn’t be regarded as competing requirements. In time, they may seemingly each turn out to be crucial layers of the bigger agentic stack as we construct more and more subtle AI programs.
The groups that acknowledge this early would be the ones that efficiently scale their agentic programs into sturdy, production-grade architectures.
