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 throughout the first month. Organizations shortly noticed the worth of MCP as a solution 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 commonplace for agentic programs.
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? Properly, not precisely. The fact is that whereas there’s some overlap, they function at completely different ranges of the agentic stack and are each extremely related.
MCP is designed as a means for LLMs to know what exterior instruments can be found to it. Earlier than MCP, these instruments had been uncovered primarily by means of APIs. Nonetheless, uncooked API dealing with by an LLM is clumsy and troublesome to scale. LLMs are designed to function on this planet of pure language, the place they interpret a job and establish the appropriate software able to carrying out it. APIs additionally undergo from points associated to standardization and versioning. For instance, if an API undergoes a model replace, how would the LLM learn about it and use it accurately, particularly when attempting to scale throughout hundreds 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 software descriptions and manifest despatched to the LLM can turn out to be huge, shortly consuming the immediate’s whole context window. This impacts even the biggest LLMs, together with these supporting a whole bunch of hundreds of tokens. At scale, this turns into a elementary constraint. Lately, there have been spectacular strides in decreasing the token rely utilized by MCP servers, however even then, the scalability limits of MCP are more likely to stay.
That is the place A2A is available in. A2A doesn’t function on the degree 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, quite than explicitly itemizing the instruments or detailed expertise the agent can entry. Moreover, A2A works solely between brokers, which means it doesn’t have the flexibility to work together immediately with instruments or finish programs the way in which MCP does.
So, which one do you have to use? Which one is healthier? In the end, 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 may possibly entry, MCP alone will be an excellent match—so long as the immediate stays compact sufficient to suit throughout the LLM’s context window (which incorporates the whole immediate funds, together with software schemas, system directions, dialog state, retrieved paperwork, and extra). Nonetheless, if you’re deploying a multi-agent system, you’ll very possible want so as to add A2A into the combination.
Think about a supervisory agent accountable for dealing with a request reminiscent of, “analyze Wi-Fi roaming issues and advocate mitigation methods.” Relatively than exposing each attainable software immediately, the supervisor makes use of A2A to find specialised brokers—reminiscent of 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 precise instruments it wants. On this stream, A2A gives scalable agent-level routing, whereas MCP gives 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 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 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 boundaries 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 site visitors from flooding the whole 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 exterior of its quick Layer-2 community, it should first uncover the closest router, understanding 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 gives 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 gives a gateway to the remainder of the agentic community.
The comparability will not be an ideal match, nevertheless 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 total stack as effectively. On this gentle, MCP and A2A shouldn’t be regarded as competing requirements. In time, they are going to possible 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.
