MCP and A2A: A Community Engineer’s Psychological Mannequin for Agentic AI

The Mannequin Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a big 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 hundreds of stars on GitHub throughout the first month. Organizations rapidly 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 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 sluggish 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 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 once more? 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 means for LLMs to know what exterior instruments can be found to it. Earlier than MCP, these instruments have been uncovered primarily by APIs. Nonetheless, 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 suitable 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 find out about it and use it appropriately, particularly when attempting to scale throughout hundreds of APIs? This rapidly turns into a show-stopper. These have 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 change into large, rapidly consuming the immediate’s complete context window. This impacts even the biggest LLMs, together with these supporting tons of of hundreds of tokens. At scale, this turns into a basic constraint. Just lately, there have been spectacular strides in decreasing the token rely 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 degree of instruments or software descriptions, and it doesn’t get entangled 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 expertise the agent can entry. Moreover, A2A works solely between brokers, which 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 must you use? Which one is best? 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 could be a great match—so long as the immediate stays compact sufficient to suit throughout the LLM’s context window (which incorporates all the immediate finances, together with software schemas, system directions, dialog state, retrieved paperwork, and extra). Nonetheless, if you’re deploying a multi-agent system, you’ll very doubtless want so as to add A2A into the combination.

Think about a supervisory agent chargeable for dealing with a request comparable to, “analyze Wi-Fi roaming issues and suggest mitigation methods.” Quite than exposing each attainable software instantly, the supervisor makes use of A2A to find specialised brokers—comparable 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 gives scalable agent-level routing, whereas MCP gives 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 determination; it’s an architectural one, the place each protocols could 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 rapidly 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 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 gives detailed visibility and direct entry, but it surely 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 might not be an ideal match, but it surely provides 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 finally require the complete stack as effectively. On this gentle, MCP and A2A shouldn’t be regarded as competing requirements. In time, they may doubtless each change into crucial layers of the bigger agentic stack as we construct more and more subtle AI methods.

The groups that acknowledge this early would be the ones that efficiently scale their agentic methods into sturdy, production-grade architectures.