MCP (Model Context Protocol) vs A2A (Agent-to-Agent Protocol) Clearly Explained - Faz Business

Why AI Agents Need a Common Language

AI is getting incredibly smart. We’re moving past single, giant AI models towards teams of specialized AI agents working together. Think of them like expert helpers, each tackling a specific task – from automating business processes to being your personal assistant. These agent teams are popping up everywhere.

But there’s a catch. Right now, getting these different agents to actually talk to each other smoothly is a big challenge. Imagine trying to run a global company where every department speaks a different language and uses incompatible tools. That’s kind of where we are with AI agents. They’re often built differently, by different companies, and live on different platforms. Without standard ways to communicate, teamwork gets messy and inefficient.

This feels a lot like the early days of the internet. Before universal rules like HTTP came along, connecting different computer networks was a nightmare. We face a similar problem now with AI. As more agent systems appear, we desperately need a universal communication layer. Otherwise, we’ll end up tangled in a web of custom integrations, which just isn’t sustainable.

Two protocols are starting to address this: Google’s Agent-to-Agent (A2A) protocol and Anthropic’s Model Context Protocol (MCP).

Google’s A2A is an open effort (backed by over 50 companies!) focused on letting different AI agents talk directly to each other. The goal is a universal language so agents can find each other, share info securely, and coordinate tasks, no matter who built them or where they run.
Anthropic’s MCP, on the other hand, tackles a different piece of the puzzle. It helps individual language model agents (like chatbots) access real-time information, use external tools, and follow specific instructions while they’re working. Think of it as giving an agent superpowers by connecting it to outside resources.

These two protocols solve different parts of the communication problem: A2A focuses on how agents communicate with each other (horizontally), while MCP focuses on how a single agent connects to tools or memory (vertically).

Getting to Know Google’s A2A

What’s A2A Really About?

Google’s Agent-to-Agent (A2A) protocol is a big step towards making AI agents communicate and coordinate more effectively. The main idea is simple: create a standard way for independent AI agents to interact, no matter who built them, where they live online, or what software framework they use.

A2A aims to do three key things:

Create a universal language all agents understand.
Ensure information is exchanged securely and efficiently.
Make it easy to build complex workflows where different agents team up to reach a common goal.

A2A Under the Hood: The Technical Bits

Let’s peek at the main components that make A2A work:

1. Agent Cards: The AI Business Card

How does one AI agent learn what another can do? Through an Agent Card. Think of it like a digital business card. It’s a public file (usually found at a standard web address like /.well-known/agent.json) written in JSON format.

This card tells other agents crucial details:

Where the agent lives online (its address).
Its version (to make sure they’re compatible).
A list of its skills and what it can do.
What security methods it requires to talk.
The data formats it understands (input and output).

Agent Cards enable capability discovery by letting agents advertise what they can do in a standardized way. This allows client agents to identify the most suitable agent for a given task and initiate A2A communication automatically. It’s similar to how web browsers check a robots.txt file to know the rules for crawling a website. Agent Cards allow agents to discover each other’s abilities and figure out how to connect, without needing any prior manual setup.

2. Task Management: Keeping Work Organized

A2A organizes interactions around Tasks. A Task is simply a specific piece of work that needs doing, and it gets a unique ID so everyone can track it.

Each Task goes through a clear lifecycle:

Submitted: The request is sent.
Working: The agent is actively processing the task.
Input-Required: The agent needs more information to continue, typically prompting a notification for the user to intervene and provide the necessary details.
Completed / Failed / Canceled: The final outcome.

This structured process brings order to complex jobs spread across multiple agents. A “client” agent kicks off a task by sending a Task description to a “remote” agent capable of handling it. This clear lifecycle ensures everyone knows the status of the work and holds agents accountable, making complex collaborations manageable and predictable.

3. Messages and Artifacts: Sharing Information

How do agents actually exchange information? Conceptually, they communicate through messages, which are implemented under the hood using standard protocols like JSON-RPC, webhooks, or server-sent events (SSE)depending on the context. A2A messages are flexible and can contain multiple parts with different types of content:

TextPart: Plain old text.
FilePart: Binary data like images or documents (sent directly or linked via a web address).
DataPart: Structured information (using JSON).

This allows agents to communicate in rich ways, going beyond just text to share files, data, and more.

When a task is finished, the result is packaged as an Artifact. Like messages, Artifacts can also contain multiple parts, letting the remote agent send back complex results with various data types. This flexibility in sharing information is vital for sophisticated teamwork.

4. Communication Channels: How They Connect

A2A uses common web technologies to make connections easy:

Standard Requests (JSON-RPC over HTTP/S): For typical, quick request-and-response interactions, it uses a simple JSON-RPC running over standard web connections (HTTP or secure HTTPS).
Streaming Updates (Server-Sent Events – SSE): For tasks that take longer, A2A can use SSE. This lets the remote agent “stream” updates back to the client over a persistent connection, useful for progress reports or partial results.
Push Notifications (Webhooks): If the remote agent needs to send an update later (asynchronously), it can use webhooks. This means it sends a notification to a specific web address provided by the client agent.

Developers can choose the best communication method for each task. For quick, one-time requests, tasks/send can be used, while for long-running tasks that require real-time updates, tasks/sendSubscribe is ideal. By leveraging familiar web technologies, A2A makes it easier for developers to integrate and ensures better compatibility with existing systems.

Keeping it Secure: A2A’s Security Approach

Security is a core part of A2A. The protocol includes robust methods for verifying agent identities (authentication) and controlling access (authorization).

The Agent Card plays a crucial role, outlining the specific security methods required by an agent. A2A supports widely trusted security protocols, including:

OAuth 2.0 methods (a standard for delegated access)
Standard HTTP authentication (e.g., Basic or Bearer tokens)
API Keys

A key security feature is support for PKCE (Proof Key for Code Exchange), an enhancement to OAuth 2.0 that improves security. These strong, standard security measures are essential for businesses to protect sensitive data and ensure secure communication between agents.

Where Can A2A Shine? Use Cases Across Industries

A2A is perfect for situations where multiple AI agents need to collaborate across different platforms or tools. Here are some potential applications:

Software Engineering: AI agents could assist with automated code review, bug detection, and code generation across different development environments and tools. For example, one agent could analyze code for syntax errors, another could check for security vulnerabilities, and a third could propose optimizations, all working together to streamline the development process.
Smarter Supply Chains: AI agents could monitor inventory, predict disruptions, automatically adjust shipping routes, and provide advanced analytics by collaborating across different logistics systems.
Collaborative Healthcare: Specialized AI agents could analyze different types of patient data (such as scans, medical history, and genetics) and work together via A2A to suggest diagnoses or treatment plans.
Research Workflows: AI agents could automate key steps in research. One agent finds relevant data, another analyzes it, a third runs experiments, and another drafts results. Together, they streamline the entire process through collaboration.
Cross-Platform Fraud Detection: AI agents could simultaneously analyze transaction patterns across different banks or payment processors, sharing insights through A2A to detect fraud more quickly.

These examples show A2A’s power to automate complex, end-to-end processes that rely on the combined smarts of multiple specialized AI systems, boosting efficiency everywhere.

Unpacking Anthropic’s MCP: Giving Models Tools & Context

What’s MCP Really About?

Anthropic’s Model Context Protocol (MCP) tackles a different but equally important challenge: helping LLM-based AI systems connect to the outside world while they’re working, rather than enabling communication between multiple agents. The core idea is to provide language models with relevant information and access to external tools (such as APIs or functions). This allows models to go beyond their training data and interact with current or task-specific information.

Without a shared protocol like MCP, each AI vendor is forced to define its own way of integrating external tools. For example, if a developer wants to call a function like “generate image” from Clarifai, they must write vendor-specific code to interact with Clarifai’s API. The same is true for every other tool they might use, resulting in a fragmented system where teams must create and maintain separate logic for each provider. In some cases, models are even given direct access to systems or APIs — for example, calling terminal commands or sending HTTP requests without proper control or security measures.

MCP solves this problem by standardizing how AI systems interact with external resources. Rather than building new integrations for every tool, developers can use a shared protocol, making it easier to extend AI capabilities with new tools and data sources.

MCP Under the Hood: The Technical Bits

Here’s how MCP enables this connection:

1. Client-Server Setup

MCP uses a clear client-server structure:

MCP Host: This is the application where the AI model lives (e.g., Anthropic’s Claude Desktop app, a coding assistant in your IDE, or a custom AI app).
MCP Client: Embedded within the Host, the Client manages the connection to a server.
MCP Server: This is a separate component that can run locally or in the cloud. It provides the tools, data (called Resources), or predefined instructions (called Prompts) that the AI model might need.

The Host’s Client makes a dedicated, one-to-one connection to a Server. The Server then exposes its capabilities (tools, data) for the Client to use on behalf of the AI model. This setup keeps things modular and scalable – the AI app asks for help, and specialized servers provide it.

2. Communication

MCP offers flexibility in how clients and servers talk:

Local Connection (stdio): If the client and server are running on the same computer, they can use standard input/output (stdio) for very fast, low-latency communication. An added benefit is that locally hosted MCP servers can directly read from and write to the file system, avoiding the need to serialize file contents into the LLM context.
Network Connection (HTTP with SSE): For connections over a network (different machines or the internet), MCP uses standard HTTP with Server-Sent Events (SSE). This allows two-way communication, where the server can push updates to the client whenever needed (great for longer tasks or notifications).

Developers choose the transport based on where the components are running and what the application needs, optimizing for speed or network reach.

3. Key Building Blocks: Tools, Resources, and Prompts

MCP Servers provide their capabilities through three core building blocks: Tools, Resources, and Prompts. Each one is controlled by a different part of the system.

Tools (Model Controlled): Tools are executable operations that the AI model can autonomously invoke to interact with the environment. These could include tasks like writing to a database, sending a request, or performing a search. MCP Servers expose a list of available tools, each defined by a name, a description, and an input schema (usually in JSON format). The application passes this list to the LLM, which then decides which tools to use and how to use them to complete a task. Tools give the model agency in executing dynamic actions during inference.
Resources (Application Controlled): Resources are structured data elements such as files, database records, or contextual documents made available to the LLM-powered application. They are not selected or used autonomously by the model. Instead, the application (usually built by an AI engineer) determines how these resources are surfaced and integrated into workflows. Resources are typically static and predefined, providing reliable context to guide model behavior.
Prompts (User Controlled): Prompts are reusable, user-defined templates that shape how the model communicates and operates. They often contain placeholders for dynamic values and can incorporate data from resources. The server programmer defines which prompts are available to the application, ensuring alignment with the available data and tools. These prompts are surfaced to users within the application interface, giving them direct influence over how the model is guided and instructed.

Example: Clarifai provides an MCP Server that enables direct interaction with tools, models, and data resources on the Platform. For example, given a prompt to generate an image, the MCP Client can call the generate_image Tool. The Clarifai MCP Server runs a text-to-image model from the community and returns the result. This is an unofficial early preview and will be live soon.

These primitives provide a standard way for AI models to interact with the external world predictably.

MCP in Action: Use Cases Across Key Domains

MCP opens up many possibilities by letting AI models tap into external tools and data:

Smarter Enterprise Assistants: Create AI helpers that can securely access company databases, documents, and internal APIs to answer employee questions or automate internal tasks.
Powerful Coding Assistants: AI coding tools can use MCP to access your entire codebase, documentation, and build systems, providing much more accurate suggestions and analysis.
Easier Data Analysis: Connect AI models directly to databases via MCP, allowing users to query data and generate reports using natural language.
Tool Integration: MCP makes it easier to connect AI to various developer platforms and services, enabling things like:
- Automated data scraping from websites.
- Real-time data processing (e.g., using MCP with Confluent to manage Kafka data streams via chat).
- Giving AI persistent memory (e.g., using MCP with vector databases to let AI search past conversations or documents).

These examples show how MCP can dramatically boost the intelligence and usefulness of AI systems in many different areas.

A2A and MCP Working Together

So, are A2A and MCP competitors? Not really. Google has even stated they see A2A as complementing MCP, suggesting that advanced AI applications will likely need both. They recommend using MCP for tool access and A2A for agent-to-agent communication.

A useful way to think about it:

MCP provides vertical integration: Connecting an application (and its AI model) deeply with the specific tools and data it needs.
A2A provides horizontal integration: Connecting different, independent agents across various systems.

Imagine MCP gives an individual agent the knowledge and tools it needs to do its job well. Then, A2A provides the way for these well-equipped agents to collaborate as a team.

This suggests powerful ways they could be used together:

Let’s understand this with an example: an HR onboarding workflow.

An “Orchestrator” agent is in charge of onboarding a new employee.
It uses A2A to delegate tasks to specialized agents:
- Tells the “HR Agent” to create the employee record.
- Tells the “IT Agent” to provision necessary accounts (email, software access).
- Tells the “Facilities Agent” to prepare a desk and equipment.
The “IT Agent,” when provisioning accounts, might internally use MCP to:

In this scenario, A2A handles the high-level coordination between agents, while MCP handles the specific, low-level interactions with tools and data needed by individual agents. This layered approach allows for building more modular, scalable, and secure AI systems.

While these protocols are currently seen as complementary, it’s possible that, as they evolve, their functionalities may start to overlap in some areas. But for now, the clearest path forward seems to be using them together to tackle different parts of the AI communication puzzle.

Wrapping Up

Protocols like A2A and MCP are shaping how AI agents work. A2A helps agents talk to each other and coordinate tasks. MCP helps individual agents use tools, memory, and other external information to be more useful. When used together, they can make AI systems more powerful and flexible.

The next step is adoption. These protocols will only matter if developers start using them in real systems. There may be some competition between different approaches, but most experts think the best systems will use both A2A and MCP together.

As these protocols grow, they may take on new roles. The AI community will play a big part in deciding what comes next.

We’ll be sharing more about MCP and A2A in the coming weeks. Follow us on X and LinkedIn, and join our Discord channel to stay updated!