The Mannequin Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a major trade consideration over the previous 12 months. MCP first grabbed the world’s consideration in dramatic trend when it was printed by Anthropic in November 2024, garnering tens of 1000’s 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 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 another time? Properly, 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 manner for LLMs to grasp what exterior instruments can be found to it. Earlier than MCP, these instruments had been uncovered primarily by way of APIs. Nonetheless, 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 activity and establish the precise device able to engaging in 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 rapidly 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 device descriptions and manifest despatched to the LLM can develop into huge, rapidly consuming the immediate’s complete context window. This impacts even the most important LLMs, together with these supporting tons of of 1000’s of tokens. At scale, this turns into a basic constraint. Not too long ago, 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 device 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, quite than explicitly itemizing the instruments or detailed expertise the agent can entry. Moreover, A2A works completely between brokers, that means it doesn’t have the flexibility to work together instantly with instruments or finish methods 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 possibly entry, MCP alone will be a perfect match—so long as the immediate stays compact sufficient to suit inside the LLM’s context window (which incorporates all the immediate funds, together with device schemas, system directions, dialog state, retrieved paperwork, and extra). Nonetheless, if you’re deploying a multi-agent system, you’ll very probably want so as to add A2A into the combination.
Think about a supervisory agent answerable for dealing with a request corresponding to, “analyze Wi-Fi roaming issues and suggest mitigation methods.” Slightly than exposing each doable device instantly, 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—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 movement, 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 will 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 had been small and self-contained, the place a single Layer-2 area (the info hyperlink layer) was enough. As networks grew and have become interconnected, the boundaries of Layer-2 had been rapidly reached, necessitating the introduction of routers and routing protocols—generally 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 all the system. On the router, networks are described in higher-level, summarized phrases, quite than exposing all of the underlying element. For a pc to speak outdoors of its quick Layer-2 community, it should first uncover the closest router, realizing that its supposed 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 might not be an ideal match, nevertheless it affords 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 ultimately require the total stack as effectively. On this gentle, MCP and A2A shouldn’t be regarded as competing requirements. In time, they are going to probably each develop into important 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.
