The Mannequin Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a major business consideration over the previous yr. MCP first grabbed the world’s consideration in dramatic style when it was revealed 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 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 permits brokers to find one another’s capabilities, enabling the speedy progress and scaling of agentic techniques.
Each protocols are aligned with the Linux Basis and are designed for agentic techniques, 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 business 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 techniques.
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 once more? Properly, not precisely. The fact 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 means of APIs. Nonetheless, uncooked API dealing with by an LLM is clumsy and tough to scale. LLMs are designed to function on this planet of pure language, the place they interpret a process and determine the suitable 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 learn about it and use it appropriately, particularly when making an attempt to scale throughout hundreds of APIs? This shortly turns into a show-stopper. These have been exactly the issues that MCP was designed to unravel.
Architecturally, MCP works nicely—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 turn out to be huge, shortly consuming the immediate’s complete context window. This impacts even the most important LLMs, together with these supporting a whole lot of hundreds of tokens. At scale, this turns into a basic constraint. Just lately, there have been spectacular strides in lowering 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 stage of instruments or instrument 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, somewhat 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 straight with instruments or finish techniques the best way 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 great match—so long as the immediate stays compact sufficient to suit throughout the LLM’s context window (which incorporates the complete immediate funds, together with instrument schemas, system directions, dialog state, retrieved paperwork, and extra). Nonetheless, in case you are deploying a multi-agent system, you’ll very probably want so as to add A2A into the combo.
Think about a supervisory agent chargeable for dealing with a request akin to, “analyze Wi-Fi roaming issues and suggest mitigation methods.” Moderately than exposing each potential instrument straight, 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 circulation, 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 choice; 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 info hyperlink layer) was adequate. As networks grew and have become interconnected, the boundaries of Layer-2 have been shortly reached, necessitating the introduction of routers and routing protocols—often called 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 complete system. On the router, networks are described in higher-level, summarized phrases, somewhat than exposing all of the underlying element. For a pc to speak outdoors of its instant Layer-2 community, it should first uncover the closest router, understanding 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 is probably not an ideal match, however 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 techniques will finally require the complete stack as nicely. On this gentle, MCP and A2A shouldn’t be regarded as competing requirements. In time, they may probably each turn out to be important layers of the bigger agentic stack as we construct more and more refined AI techniques.
The groups that acknowledge this early would be the ones that efficiently scale their agentic techniques into sturdy, production-grade architectures.