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 option 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 development 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 fast adoption, whereas A2A’s progress has been extra of a sluggish 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 customary 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 another time? Nicely, 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 grasp what exterior instruments can be found to it. Earlier than MCP, these instruments had been uncovered primarily via 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 job and determine the best software able to carrying out 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 accurately, particularly when making an attempt 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 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 develop into 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 elementary constraint. Not too long ago, 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 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, fairly than explicitly itemizing the instruments or detailed abilities the agent can entry. Moreover, A2A works solely between brokers, which means it doesn’t have the power to work together immediately with instruments or finish techniques the best way MCP does.
So, which one must you use? Which one is healthier? In the end, the reply is each.
If you’re constructing a easy agentic system with a single supervisory agent and a wide range of instruments it might probably 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 all the immediate funds, together with software schemas, system directions, dialog state, retrieved paperwork, and extra). Nevertheless, if you’re deploying a multi-agent system, you’ll very probably want so as to add A2A into the combo.
Think about a supervisory agent liable for dealing with a request corresponding to, “analyze Wi-Fi roaming issues and advocate mitigation methods.” Quite than exposing each doable software immediately, 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 precise instruments it wants. On this movement, A2A supplies scalable agent-level routing, whereas MCP supplies 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 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 had been small and self-contained, the place a single Layer-2 area (the information hyperlink layer) was enough. 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 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, fairly 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 supplies 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 supplies a gateway to the remainder of the agentic community.
The comparability is probably not 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 techniques will finally require the total stack as properly. On this mild, MCP and A2A shouldn’t be regarded as competing requirements. In time, they’ll probably each develop into crucial 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.
