Complementary Protocols for Agentic Systems : Understanding Google’s A2A & Anthropic’s MCP
The emergent multi-agent applications rely on sophisticated language models and often need to interact with other systems, built on other frameworks and crossing organizational boundaries.
Two distinct protocols play critical roles in enabling these capabilities: Anthropic’s Model Context Protocol (MCP) and our recently Google’s proposed Agent-to-Agent (A2A) interoperability protocol. Both relate to agentic AI, but, they address different levels and aspects of agentic system design. During our Google NEXT 2025 conference, after we annoucned this, as an author of this protocol, I was asked many questions by our customers and partners and academics. One of the most salient questions were around how these protocols compare and contrast. This article aims to clarify their specific functions, differences, and how they can complement each other.
Use Case: The Smart Personal Assistant & External Services
Let’s start with an example of a Personal Assistant Agentic System (PAAS) designed to manage your daily activities. This PAAS is built using an Anthropic language model (like Claude) at its core. The PAAS needs to interact with external services, like a Restaurant Reservation Agent System (RRAS) run by “The Gourmet Spot”.
The Protocols
Let’s take a look at the major protcols and how they relate and complement each other.
Anthropic’s Model Context Protocol (MCP)
Structuring Interaction with the Language Model
Anthropic’s MCP, serves a specific and vital function within an agentic application built using language models. Its core purpose is to define the structured format for the direct communication that occurs between the application’s coordinating logic (often called an orchestrator) and the language model (LLM) itself, such as Claude. MCP’s scope is firmly internal, governing the immediate interface used to send information to and receive instructions from the LLM within that single application.
Functionally, MCP provides the blueprint for how the application should package all necessary information for the model to effectively process a request. This includes formatting system prompts that provide overall guidance, structuring user messages and the ongoing conversation history, and clearly defining the available tools (like internal APIs for checking a calendar or sending an email) that the model might need to use. Critically, MCP also dictates how the model structures its response. This might be straightforward text generation, or it could be a specific, formatted request from the model indicating its intent to use one of the defined tools. Subsequently, when a tool is executed by the application, MCP defines how the results of that execution should be formatted and presented back to the model for further processing or summarization.
To illustrate, consider a Personal Assistant Agentic System (PAAS) built with an Anthropic model. When the user asks, “What’s my schedule like tomorrow afternoon?”, the PAAS orchestrator employs MCP. It meticulously formats the user’s query, relevant chat history, and the definition of its internal check_calendar tool according to MCP specifications. This package is then sent to the Anthropic LLM. If the model determines it needs the calendar data, its response, also structured via MCP, will signal a request to invoke the check_calendar tool, potentially specifying parameters like the date and time range. Once the orchestrator runs the tool and retrieves the schedule, it again uses MCP to format these results before sending them back into the model’s context.
The model can then use this information to generate a helpful, natural language summary for the user. In essence, MCP is fundamentally concerned with the precise structuring of the input and output data flowing directly between the application and the LLM during each processing cycle, ensuring the model has the context and capability instructions it needs.
Google’s Agent-to-Agent Interoperability Protocol (A2A)
Facilitating Collaboration Between Systems
In contrast to MCP’s internal focus, Google’s proposed Agent-to-Agent interoperability protocol (A2A) tackles the challenge of external communication and collaboration. Its objective is to establish a standardized methodology enabling independent, distinct agent systems — which may be developed by different organizations using varied underlying technologies — to effectively communicate, negotiate tasks, and work together. A2A aims to foster interoperability across autonomous systems that need to interact to achieve user goals.
The scope of A2A is therefore external, designed to facilitate communication between separate agent systems. Imagine our PAAS needing to interact with an external Restaurant Reservation Agent System (RRAS). A2A provides the framework for this interaction. Its functionality encompasses defining standards for several crucial aspects of inter-agent collaboration. This includes methods for agents to discover one another, protocols for establishing secure communication sessions, and standardized message formats for conveying requests, making offers, sending confirmations, or reporting errors.
Also, A2A intends to standardize how agents negotiate their capabilities and the specific parameters of a collaborative task. It also addresses the need to manage the lifecycle of these tasks, which might involve multiple steps or asynchronous operations requiring coordination across the independent systems involved.
Using our example, after the PAAS (internally leveraging its LLM via MCP) understands the user’s desire to book a restaurant, it must engage the external RRAS. This external dialogue would be governed by A2A. The PAAS would utilize A2A mechanisms to find and initiate a session with the RRAS agent. It would then send a structured A2A message, perhaps formatted as a REQUEST, detailing the booking need (party size, approximate time, preferences like a quiet table). The RRAS would reply using A2A-defined message formats, potentially sending an OFFER presenting available time slots matching the criteria.
If the user accepts an option via the PAAS, the PAAS would send a final A2A CONFIRM message to the RRAS to secure the booking. Throughout this exchange, A2A governs the structured conversation and workflow between the two separate systems, providing a common language that enables them to collaborate effectively, regardless of their internal architectures or the specific protocols (like MCP) they might use for their internal processing.
Synergies: How MCP and A2A Complement Each Other
It is important to note that MCP and A2A operate at two different levels and are highly complementary in a complete multi-agent system.
- Internal Processing (MCP): The PAAS uses MCP to manage its internal “thought process” powered by the Anthropic LLM. It uses MCP to understand user requests, decide on actions, and formulate requests to use internal tools or decide to contact an external agent.
- External Communication (A2A): When the PAAS (guided by its LLM via MCP) decides it needs external help (e.g., booking a table), the PAAS’s orchestrator switches to using the A2A protocol to communicate with the relevant external system (RRAS).
- Bridging the Gap: The PAAS orchestrator acts as the bridge. It takes the intent generated by the LLM (expressed potentially via MCP tool use or text output) and translates it into a formal A2A message. Conversely, it receives A2A messages from external agents, processes them, and potentially formats the relevant information using MCP to update its internal LLM for the next step (e.g., “I received confirmation from the RRAS, now tell the user”).
Conclusion
Anthropic’s MCP and Google’s A2A are not competing protocols; A2A ❤️ MCP.
They solve different, complementary, but essential problems in the multi-agent systems space. One, MCP, is more focused on getting Agents access to resources and the other, A2A, on a higher order scenario of multi-agent collaboration and reasoning.
- MCP standardizes how an application interacts with its Anthropic LLM, providing the necessary context and tool structure for the model to reason and act effectively within that application.
- A2A standardizes how separate agent applications interact with each other, often across enterprises; enabling a decentralized ecosystem of collaborating agents.
A robust agentic system like our PAAS would likely use MCP for its internal LLM interactions and A2A for its external communications with other services like the RRAS. Both are vital building blocks for the future of intelligent, interconnected applications.
