| Category | MCP (Model Context Protocol) | A2A (Agent-to-Agent Protocol) |
|---|---|---|
| Primary Function | Provides tools and data to a single AI model to enhance its context. | Enables multiple autonomous AI agents to communicate and delegate tasks. |
This article briefly discusses how MCP and A2A can be used to enable scalable, interoperable AI systems.
Artificial intelligence systems are growing more complex and distributed. There is an increasing need for standardized, modular protocols that allow AI agents to access tools, data, and collaborate effectively.
AI applications expand beyond single-task systems into complex, multi-agent environments, and challenges related to interoperability, context-awareness, and modular integration have become more pronounced.
These issues derived from long-standing problems in distributed systems and service-oriented architecture (SOA), particularly the MxN integration challenge, where M models must integrate with N tools or data sources, requiring M*N custom connectors. Without standards, this leads to technical debt, increased complexity, lack of composability, and ultimately lack of speed in delivery.
Two emerging proposals addressing these challenges are Anthropic's Model Context Protocol (MCP) and Google's Agent2Agent (A2A) framework.
MCP facilitates standardized interactions between large language models (LLMs) and external tools, tackling the longstanding MxN integration problem, possibly familiar to those who experienced the early 2000's middleware Service Bus approach. A2A, on the other hand, provides a shared metadata and communication format for autonomous agents to coordinate and exchange tasks.
The MxN integration problem is not unique to AI. In the SOA era, similar concerns emerged when web services needed to integrate across platforms. Enterprise Service Buses (ESBs) and later REST APIs attempted to solve this through standardization. However, AI presents new complications: LLMs operate in probabilistic, context-sensitive ways that differ from deterministic service calls.
Anthropic’s MCP (2024) offers a formal specification for describing tools, functions, and external data sources in a machine-readable format. This metadata can then be programmatically embedded into the model’s context window. MCP includes: Toolcards, Context injection rules, and Runtime orchestration.
Instead of creating custom integrations for every model and tool, developers clearly describe the available tools, accessible context, and possible actions using language the model easily understands. This allows models, in theory, to dynamically understand and use any tool, just as plugging a USB stick into a laptop lets it immediately become usable.
When people refer to Anthropic’s MCP as the "USB of AI", they’re drawing a comparison that, on the surface, is both intuitive and evocative, but not necessarily correct. Just as USB revolutionized hardware connectivity by offering a universal, standardized interface between devices and computers, MCP aspires to bring that same kind of seamless interoperability to the world of AI models, tools, and data systems.
At its core, MCP provides a common structure, based on JSON schemas, that allows different AI models to interact with external tools, functions, and context objects in a standardized way. Instead of creating custom integrations for every model and tool, developers clearly describe the available tools, accessible context, and possible actions using language the model easily understands.
This allows models, in theory, to dynamically understand and use any tool, just as plugging a USB stick into a laptop lets it immediately become usable.
This analogy helps emphasize the promise of MCP: universality, simplification, and scale. Just like USB transformed the hardware ecosystem by eliminating the need for dozens of different connectors and ports, MCP aims to standardize how AI agents connect to external services, perform actions, and retrieve or store knowledge. It abstracts the complexity of integrating AI with real-world tasks in the same way USB abstracts the details of how a device communicates with a computer.
However, while the comparison is useful, it has its limits—and important ones. USB works because it is based on deterministic behavior and strict physical standards. Plug in a device, and it either works or it doesn’t. The behavior is consistent, reproducible, and testable.
MCP, by contrast, operates in the realm of semantics, probability, and reasoning. It doesn’t enforce behavior; it merely describes it in a structured way that a language model must interpret and act upon. And that’s where things can go sideways. Two different models might interpret the same MCP tool schema differently—or fail to use it at all. The success of MCP depends on the understanding, reliability, and reasoning capabilities of the underlying model, not on a strict enforcement layer.
Moreover, USB connections are secure and sandboxed within an operating system’s driver framework. MCP, on the other hand, operates in a much looser environment where models may call tools, APIs, or access memory in ways that are hard to fully control or isolate.
There is no built-in mechanism to guarantee that a model will use a tool safely, correctly, or even at all—meaning MCP can't offer the same confidence or robustness that USB provides.
Finally, USB is indifferent regards who made the computer or the device, compliance ensures universal functionality. MCP still inherits the quirks and limitations of each individual language model. What works smoothly with Claude might fail with ChatGPT, and vice versa, depending on how each model interprets the tool instructions and manages memory.
So, while calling MCP the “USB of AI” captures the ambition of standardization and interoperability, it also glosses over the reality that this kind of plug-and-play in AI is far more fragile, probabilistic, and context-dependent. It’s not yet a universal port you can trust to always work the same way—but it’s a step toward that future.
A2A’s purpose is to serve as a universal interoperability layer for agentic AI, enabling organizations to combine agents from various providers into powerful composite systems and manage them via a common framework.
Google’s Agent2Agent (A2A) Protocol is a newly announced open standard (first unveiled in April 2025) that aims to enable seamless communication and collaboration between autonomous AI agents.
The core vision behind A2A is to break down the silos between AI agents built by different vendors or frameworks, allowing them to “talk” to each other and work together on complex tasks. In enterprise settings today, agents often operate in isolation: one agent might handle IT service requests while another manages inventory, yet they cannot easily coordinate.
A2A addresses this by providing a common language for agents, much like HTTP did for web clients and servers. With A2A, an agent can dynamically delegate subtasks to other specialized agents, exchange information, and jointly drive workflows that no single agent could accomplish alone.
The ultimate promise is a multi-agent ecosystem where AI agents regardless of their underlying technology can seamlessly collaborate to automate complex enterprise workflows, yielding unprecedented gains in productivity and efficiency. Google explicitly positions A2A as complementary to Anthropic’s MCP: while MCP standardizes how an agent connects to tools and structured data, A2A standardizes how multiple agents interact as peers.
In short, A2A’s purpose is to serve as a universal interoperability layer for agentic AI, enabling organizations to combine agents from various providers into powerful composite systems and manage them via a common framework. This vision is shared by a broad industry coalition, as evidenced by the dozens of partner companies that co-developed and endorsed the protocol from its launch.
A2A offers a layer of abstraction like HTTP. The A2A protocol defines a structured message schema that facilitates rich, multimodal communication between agents. Each interaction revolves around a clearly defined task, which agents manage dynamically through flexible client-server roles. Agents can exchange text, images, structured JSON, and other content types within a standardized, context-aware dialogue, allowing sophisticated workflows, iterative task execution, and multi-step collaborations.
Security in A2A relies on established web standards (HTTPS, OAuth2, API keys), ensuring secure and authenticated communication without exposing internal agent logic. The protocol is inherently enterprise-friendly, leveraging JSON-RPC, HTTP, and Server-Sent Events (SSE), thus integrating seamlessly into existing IT infrastructure and workflows.
Google’s initiative is strongly supported by over 50 industry leaders, including enterprise technology providers (Salesforce, Microsoft, SAP), AI specialists (Cohere), and global consulting firms (Accenture, Deloitte). Such extensive backing significantly boosts the protocol’s potential for widespread adoption and positions A2A as a foundational standard in multi-agent interoperability.
While the protocol’s vision of agent collaboration is powerful, several challenges remain. A2A currently lacks maturity and comprehensive tooling, requiring substantial additional infrastructure to handle complex orchestration, task management nuances, and large-scale agent networks.
Trust and security beyond basic authentication—especially in inter-organizational scenarios—present unresolved issues that need supplementary mechanisms like reputation systems or detailed trust frameworks.
Performance and scalability are yet to be tested at high volumes of multi-agent interactions, raising concerns over efficiency and robustness. Furthermore, A2A does not inherently enhance agent intelligence, meaning poorly designed or flawed agents could compound errors through inter-agent communications. Finally, there is a risk of standard fatigue, as developers face multiple evolving interoperability methods and no clear “killer app” yet demonstrating compelling real-world benefits.
Despite these challenges, Google's A2A protocol represents a significant advancement toward an interoperable AI ecosystem. If successfully adopted, it could unlock considerable innovation in how AI agents coordinate to solve complex enterprise problems collaboratively. Its future depends on the ongoing development of complementary orchestration tools, trust frameworks, and widespread community-driven refinement.
| Category | MCP (Model Context Protocol) | A2A (Agent-to-Agent Protocol) |
|---|---|---|
| Primary Function | Provides tools and data to a single AI model to enhance its context. | Enables multiple autonomous AI agents to communicate and delegate tasks. |
| Structure | Injects structured information like tool descriptions directly into the model's prompt. | Uses standardized "Agent Cards" and formal messages for inter-agent interaction. |
| Scope | Focuses on intra-agent augmentation, making one agent smarter and more capable. | Focuses on inter-agent coordination, allowing different agents to work together. |
| Inspiration | Acts like a "USB for Models," offering plug-and-play access to external systems. | Functions like an "RPC for Agents," standardizing communication between distributed entities. |
| Maturity | Is a widely adopted standard integrated into major AI toolchains as of 2025. | Is a newer, Google-led initiative from early 2025 still building its ecosystem. |
MCP is primarily designed to serve as a contextual data delivery protocol for LLMs. Its main goal is to standardize how structured data, APIs, tools, and memory systems are made accessible to language models at runtime.
MCP doesn’t govern agent behavior but instead simplifies how a model can “see” and utilize external information by injecting relevant tool documentation and memory via structured, model-readable contexts.
By contrast, Google’s A2A protocol facilitates inter-agent communication. It introduces standardized ways for autonomous agents to exchange structured messages, delegate tasks, and negotiate roles. A2A is akin to a coordination layer that enables agents to form temporary or persistent collaborations by sending each other semantically defined task requests and responses. It’s less about internal cognition and more about external coordination.
MCP operates through a structured, composable format to inject various types of external context into LLM prompts. These include:
These elements are injected during the prompt construction phase, effectively augmenting the model’s limited native context window.
On the other hand, A2A defines a more formal agent interface. Its key components are:
A2A emphasizes typed, machine-interpretable interactions between independent computational entities.
MCP is scoped around intra-agent augmentation—providing a single model instance with the ability to reason across external knowledge bases, tools, and memory. It is tightly coupled with the model execution pipeline and is not concerned with multi-agent orchestration.
A2A, by contrast, addresses inter-agent collaboration. It supports architectures where agents can discover, message, and coordinate with other agents asynchronously or synchronously. The protocol abstracts lower-level transport concerns and focuses on intent-driven communication, e.g., "Agent A delegates a summarization task to Agent B," or "Agent C rejects a request based on policy constraints." In essence, MCP enhances the individual model, whereas A2A enables cooperative intelligence.
MCP aims to abstract away the idiosyncrasies of tool and data source integration, allowing plug-and-play access to external systems—much like connecting a mouse or a webcam to any modern device just works.
Google’s A2A is inspired more by HTTP and RPC frameworks, where interoperability and standard method invocation between distributed services are key. It aims to define common request/response formats and capability declarations—akin to APIs for autonomous agents—to enable modular, composable ecosystems.
The metaphor is pertinent: MCP is about uniform context provision, while A2A is about standardized message-passing and distributed task execution.
MCP has reached a higher level of maturity as of 2025. It has been open-sourced, widely adopted in production by Anthropic and—more significantly—integrated into OpenAI’s tool use framework, marking a rare instance of protocol convergence between major labs. A growing number of agentic toolchains (like LangChain, AutoGen, and Haystack) have begun to support MCP, making it a de facto standard for LLM-environment interfacing.
A2A, on the other hand, was introduced in early 2025 as a Google-led initiative. It has already onboarded 50+ industry and academic partners, but it remains under active development. While promising, A2A is still in the ecosystem-building phase, and details on versioning, transport security, or runtime specifications are evolving.
The MCP and A2A protocols represent two pillars of agentic infrastructure:
They are not mutually exclusive. In fact, a robust agent system may leverage both: MCP for equipping individual agents with powerful reasoning capabilities, and A2A for orchestrating collaborative behaviors across agents.
As 2025 continues to unfold, these protocols may define the backbone of the emerging agent ecosystem, much as TCP/IP once did for the internet.