Complementary Protocols for Agentic Systems: Understanding Google’s A2A & Anthropic’s MCP

Hello Data Pills readers,

Welcome back to another deep dive into the evolving world of AI and data innovation. In today’s edition, we’re exploring how emerging protocols are shaping the future of agentic systems — making multi-agent collaboration more powerful and interoperable than ever before.

Let’s get into it!

As agentic AI systems become more complex and interconnected, the need for robust communication and interoperability standards is critical. Today's multi-agent applications not only rely on powerful language models but also demand seamless interaction across diverse frameworks and organizational boundaries. In this evolving landscape, two protocols have emerged as key enablers: Anthropic’s Model Context Protocol (MCP) and Google’s newly proposed Agent-to-Agent (A2A) interoperability protocol.While both protocols aim to strengthen the agentic ecosystem, they operate at different layers and address distinct challenges in system design. Understanding how MCP and A2A complement each other is essential for anyone looking to build the next generation of collaborative AI solutions.

Use Case: The Smart Personal Assistant & External Services

Imagine a Personal Assistant Agentic System (PAAS) designed to organize your calendar, book appointments, and handle personal tasks. At its core sits a powerful language model—say, Claude by Anthropic—coordinating a network of sub-agents (for tasks like scheduling, reminders, or travel planning).

ut the real power of this system unfolds when it interacts with external agents. Suppose you want to book a table at your favorite restaurant, “The Gourmet Spot”. The PAAS needs to connect with an external Restaurant Reservation Agent System (RRAS) managed by the restaurant itself. This interaction spans organizational boundaries, technologies, and architectures.

The Protocols at Work

Here’s where interoperability becomes essential. Anthropic’s Model Context Protocol (MCP) manages internal coordination—connecting sub-agents and accessing tools and context data. Meanwhile, Google’s Agent-to-Agent (A2A) protocol allows the PAAS to communicate with black-box agents like the RRAS, even if they’re built using different stacks or are independently operated.

Together, MCP and A2A make this ecosystem possible. One ensures internal consistency and resource access. The other opens secure, structured dialogues between autonomous systems.

A Quick Recap: Anthropic’s Model Context Protocol (MCP)

As we’ve explored in earlier editions of Data Pills, Anthropic’s Model Context Protocol (MCP) plays a central role in agentic systems that rely on language models like Claude. While agent-based architectures often focus on coordination across agents or services, MCP is concerned with something more internal — the structured interaction between an application and its own language model.

At its core, MCP defines how the application’s orchestrator (the component that manages logic, memory, and tool usage) communicates with the LLM. It specifies how to format a complete "context package" for the model: this includes system-level instructions, the conversation history, current user messages, and a catalog of tools the model can call — such as internal APIs or functions.

But MCP doesn’t stop at input formatting. It also dictates how the model should respond. If the LLM decides to call a tool (e.g., a calendar-checking function), the output must follow a structured format that signals intent and provides any needed parameters. After the orchestrator runs the tool, MCP again comes into play: it defines how the results are fed back into the model’s context, allowing it to incorporate the new information into its reasoning and generate a final response — often natural language text for the user.

Let’s go back to our favorite illustrative use case: the Personal Assistant Agentic System (PAAS). Suppose the user asks, “What’s my schedule like tomorrow afternoon?” Behind the scenes, the orchestrator formats this query using MCP: it includes the user prompt, any relevant history, and details of the available check_calendar tool. The LLM, following the MCP structure, might respond with a request to call that tool, specifying a time range. The orchestrator executes the call, receives the result, and feeds it back — again via MCP — so that the model can generate a helpful, human-readable summary.

In essence, MCP is the blueprint for every interaction loop between the application and the model. It ensures structure, clarity, and continuity in multi-turn reasoning and tool usage. Importantly, MCP is not about cross-agent communication — that’s where the A2A protocol comes in, which we’ll explore next.

Google’s Agent-to-Agent Protocol (A2A): Enabling External Collaboration

While Anthropic’s Model Context Protocol (MCP) focuses on structuring internal communication between an application and its language model, Google’s Agent-to-Agent (A2A) protocol addresses a broader and more outward-looking challenge: how independent agent systems can collaborate with one another across technical, organizational, and architectural boundaries.

This is essential in real-world agentic ecosystems, where no single agent has all the tools or data needed to fulfill complex user requests.

A2A facilitates communication between a "client" agent and a “remote” agent. A client agent is responsible for formulating and communicating tasks, while the remote agent is responsible for acting on those tasks in an attempt to provide the correct information or take the correct action. This interaction involves several key capabilities:

• Capability discovery: Agents can advertise their capabilities using an “Agent Card” in JSON format, allowing the client agent to identify the best agent that can perform a task and leverage A2A to communicate with the remote agent.
• Task management: The communication between a client and remote agent is oriented towards task completion, in which agents work to fulfill end-user requests. This “task” object is defined by the protocol and has a lifecycle. It can be completed immediately or, for long-running tasks, each of the agents can communicate to stay in sync with each other on the latest status of completing a task. The output of a task is known as an “artifact.”
• Collaboration: Agents can send each other messages to communicate context, replies, artifacts, or user instructions.
• User experience negotiation: Each message includes “parts,” which is a fully formed piece of content, like a generated image. Each part has a specified content type, allowing client and remote agents to negotiate the correct format needed and explicitly include negotiations of the user’s UI capabilities–e.g., iframes, video, web forms, and more.

Use Case Anchor: What Is a PAAS?

To ground this idea, let’s return to our reference system: a Personal Assistant Agentic System (PAAS).

A PAAS is a type of agentic application built around a language model (like Claude or Gemini) that orchestrates various tasks on behalf of the user. It might coordinate sub-agents (e.g., calendar manager, email sender) to help with things like planning your day, booking a trip, or summarizing a meeting.

While the MCP protocol helps the PAAS manage internal interactions — like deciding which tool to invoke based on a user question — the A2A protocol comes into play when the PAAS needs help from another system entirely, such as a restaurant’s reservation agent or a third-party booking assistant.

Why A2A Matters

A2A is designed to standardize collaboration between autonomous agents, even if they were developed by different vendors, operate on different infrastructures, or speak different internal “languages.”

Let’s say the user asks their PAAS: “Book a table for four at The Gourmet Spot tomorrow evening.”

Internally, the PAAS uses MCP to understand the intent. But it doesn’t have direct access to the restaurant’s booking system — that’s where A2A takes over.

• First, the PAAS discovers the Restaurant Reservation Agent System (RRAS) by retrieving its Agent Card — a kind of digital resume in JSON format listing the RRAS’s capabilities.
• Then, it sends a structured REQUEST message using A2A.
• The RRAS replies with an OFFER (e.g., available times).
• Upon user confirmation, the PAAS sends a CONFIRM message to finalize the reservation.

Throughout this dialogue, A2A ensures both agents can understand each other, maintain task consistency, and communicate securely.

Core Features of A2A

A2A is built on open, web-based standards to maximize accessibility and integration. Its core capabilities include:

• Capability Discovery Agents can describe what they can do via Agent Cards, making it easy for client agents to find the right collaborator.
• Task and State Management A2A defines a structured way to represent and track tasks — whether they’re short-lived or long-running. Agents can update each other on progress or completion status, and the output is captured in structured artifacts.
• User Experience Negotiation Agents exchange information not just as text, but as "parts" — media, web elements, or formatted content. They can negotiate what UI elements each supports, like videos, forms, or iFrames.
• Secure Collaboration Built with enterprise needs in mind, A2A supports authentication and authorization standards like those in OpenAPI.
• Modality Agnostic Beyond text, A2A supports audio, video, and other data types, enabling rich multi-modal interaction.
• Based on Open Standards A2A uses well-known protocols like HTTP, SSE, and JSON-RPC, making it compatible with modern tech stacks.

A2A + MCP = True Agentic Interoperability

To build intelligent, collaborative agents, we need both MCP and A2A:

• MCP makes sure each agent knows how to talk to its own brain (the LLM).
• A2A makes sure multiple agents can work together, even if they come from different worlds.

It is important to note that MCP and A2A operate at two different levels and are highly complementary in a complete multi-agent system.

1. 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.
2. 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).
3. 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”).

The future of AI won't be one model doing everything. It will be a network of specialized, interoperable agents, working together — and protocols like A2A and MCP are the foundation.

Thanks for joining me in this deep dive into MCP and A2A — two complementary protocols shaping the future of agentic AI. As these systems evolve from isolated tools into interconnected collaborators, understanding how they communicate (internally and externally) will be key for designing scalable, intelligent applications. Stay curious, and see you in the next Data Pill!