When to Use MCP vs A2A — And Why You’ll Often Use Both

There’s been a lot of discussion recently about whether to use MCP or A2A, what they actually do, and when each should be used. In this article, we’ll break down what they are, how they work, and why in many scenarios you’ll likely end up using both together.

What Does Our AI Application Actually Do?

Let’s start with our AI application. If we’re talking about generative AI, then our application interacts with a language model. This could be a large language model (LLM) or a smaller one. These models have been pre-trained on vast datasets, and based on probability distributions, they generate one token after another to form responses.

• Our application sends it a prompt, and the model returns a response.

But we must remember that:

• These language models are trained on a finite corpus of knowledge.
• They have a cutoff date, meaning they only know information up to a certain point in time.
• Their responses are based solely on their training data and what you include in the prompt.

Enhancing the Model’s Knowledge with RAG

To make our application truly useful, we often need to provide the LLM with additional knowledge — information it can’t possibly know from its training data.

We achieve this through retrieval-augmented generation (RAG), which might involve specialized methods like graph RAG that focus on relationships between data.

• We might have a vector database that helps retrieve relevant information based on the meaning of the query.
• This database becomes a body of knowledge that we can query to supplement the LLM’s prompt.

For example:

“Give me a summary of this research paper.”

We’d include the research paper’s details in the prompt, enabling the LLM to produce a much more accurate summary.

Introducing Tools Into the Mix

Besides extra data, we might also want to give the LLM access to tools.

• To do this, we’d describe the tool in the prompt:

“This tool can perform XYZ; you can ask me to run it, and I’ll send back the result.”

But this brings challenges:

1. Each tool and knowledge source has its own way of communicating. Our application must know how to speak to each, which protocols to use, how to interpret results, and even how to describe them to the LLM.
2. Providers would otherwise need to write custom code for every platform or app, which is costly and inefficient.

Enter MCP: The Model Context Protocol

This is where the Model Context Protocol (MCP) comes in.

What is MCP?

• It’s a standardized way for an AI application to interact with different kinds of knowledge sources and tools.
• It can even give information about good prompt structures to use.

How does MCP work?

It uses a client-server architecture:

• Your application has an MCP client.
• There’s an MCP server for each particular tool or knowledge resource.
• The client talks to the server using the MCP protocol, while the server knows how to speak to its specific underlying system (e.g., a database or a tool API).

This means:

• Your app only needs to speak MCP.
• You don’t need to know anything about each tool’s internal protocols — it’s all abstracted away.

For providers:

• They only need to build one MCP server for their resource, instead of multiple plugins for every platform.

Local vs Remote MCP Servers & Security

• MCP servers can run locally (e.g., via a container) or remotely (communicating over TLS with server-sent events).
• Originally, MCP lacked authentication, but it’s now updated: MCP servers act as OAuth resource servers, so you can use standard identity providers like Entra ID, Okta, or any OAuth-compliant IDP for secure token-based auth.

Reflection: Discovering What’s Available

A powerful feature of MCP is reflection:

• Your app can ask the MCP client:

“What do you do?”

• The MCP server responds with details like: What resources (e.g., structured data, documents for RAG) it provides. What tools it offers. What prompt templates it can supply.

This means:

• Your app can include this in the system prompt for the LLM, telling it:

“These are the tools and knowledge available.”

So the LLM knows when to say:

“Call this tool with these parameters.”

In Summary: Why MCP?

MCP gives your app a standard, simple way to integrate with knowledge and tools, boosting the LLM’s capabilities without you having to build custom interfaces.

Nearly every serious AI app today will involve some external knowledge or tools, so most will leverage MCP.

What About Agents? Enter A2A (Agent to Agent)

Now let’s say your AI application isn’t just an app, but actually an agent.

• An agent might be prompted by a human or operate autonomously.
• We generally keep agents focused on a specific task, since broad multi-purpose agents often hallucinate or get confused.

But if an agent has a focused task, it will often need to collaborate with other agents to complete its work.

The Role of A2A

Imagine this:

• Agent A needs to summarize a document, then schedule a meeting.
• It might call Agent B to summarize the doc, then Agent C to schedule the meeting with key points.

This raises two problems:

1. How does Agent A know the capabilities of Agent B or C?
2. How do they communicate securely and effectively?

That’s where A2A comes in.

• A2A is a JSON-based protocol (typically JSON-RPC 2.0 over HTTPS) for agent-to-agent communication.
• It standardizes: How agents discover each other’s capabilities. How they authenticate and talk to each other.

Agent Cards: Like Digital Business Cards

When two agents connect, they exchange agent cards:

• These include: The agent’s name and version. Its skills and features (streaming support, push notifications, etc.). Auth mechanisms.

The agent card is typically hosted at a well-known URL, making onboarding simple.

Task-Based Interaction

• Agents communicate around tasks, sending messages that could be: Text, images, audio, video, structured data (like JSON).
• They might share context, collaborate over multiple turns, and run long asynchronous tasks.

When finished, they exchange artifacts, which are the final results.

So When Do You Use MCP vs A2A?

✅ Use MCP when your app or agent wants to talk to external knowledge or tools. It abstracts all the details of specific protocols.

✅ Use A2A when your agent needs to collaborate with other agents, leveraging a common language and discovery mechanism.

In practice, you’ll almost always use both:

• MCP for hooking into tools and data sources.
• A2A for coordinating with other agents.

Final Thoughts

So don’t think of it as choosing MCP or A2A. They do different things:

• MCP: Integrates your app or agent with tools & knowledge.
• A2A: Lets agents find, understand, and work with each other.

Used together, they give your AI ecosystem a powerful, secure, standardized way to expand capabilities, so your language model can perform richer, more complex tasks.