What MCP Really Does
MCP was initially developed by Anthropic to solve a particular problem: How can an AI agent running on your machine talk with Microsoft Word, browsers, and other local applications?
You can think of MCP as a standardized way for local applications and agents to communicate. The inspiration comes from the Language Server Protocol. Instead of every tool needing to support every possible agent interface, MCP creates a middle layer. If the agent and the tool both speak MCP, they can communicate, regardless of which company built them.
This abstraction is powerful. It means agents don't need to know every individual API or scripting language. They just need to speak MCP.
What A2A Was Built For
While MCP is designed for communicating with tools, A2A is intended for interacting with other agents, often over the Internet.
If MCP is about plugging into a local app, A2A is about enabling peer-to-peer communication between intelligent agents. A2A includes built-in features for authentication, message passing, and coordination, making it ideal for distributed multi-agent systems.
Imagine you're building a travel assistant. With A2A, your agent doesn't have to scrape websites or simulate a user interface. It can just talk directly to another agent, like a hotel agent or a car rental agent who knows how to handle its own internal systems. It's clean, scalable, and future-ready.
Why These Protocols Sound Similar But Aren't
It's easy to confuse MCP and A2A because they can both be stretched to do similar things. Developers are already using MCP to talk to web services and bolting authentication on top. Similarly, you could use A2A locally or build agents that just act like thin wrappers around APIs.
But these are workarounds, not design goals.
The critical distinction is intent.
- MCP is designed to make it easy for AI agents to connect to tools, especially those on the same machine or within a secure enterprise environment.
- A2A is designed to make it easy for AI agents to talk to other AI agents across domains, vendors, and systems.
You can blur the lines, but doing so often adds complexity, not clarity.
The Travel Agent Example: Where Each Protocol Shines
Let's say you're building a system to help users book a vacation. Here's how you might architect it with both protocols.
- On the backend, the airline company builds an MCP server that wraps its flight booking API. This allows any MCP-compatible agent to request flights without needing to know the company's internal systems.
- On the front end, the user has an AI assistant that converses with them in a conversational manner. It looks around and finds an available travel agent (also using A2A), which acts as the intermediary.
- That travel agent, in turn, uses MCP to communicate with specific tools, such as the airline's MCP server, a hotel's API, or a car rental service.
Now, you have a fully agentic system, where A2A connects distributed actors, and MCP connects them to the tools that enable work to be done.
This is the kind of interoperability that the future of agent-based software depends on.
What This Means for Builders
If you're developing agentic applications, the key takeaway is this: Protocols like MCP and A2A are not about ease; they're about agreement.
They make it possible for different tools and agents to speak the same language. That doesn't mean they're simple. In fact, setting them up can be complex. But what you gain is connectivity.
- Build on A2A if you want your agents to participate in a broader ecosystem of autonomous services.
- Build on MCP if your agents need to control software, run local tools, or plug into enterprise systems.
Use both when you want agents that can talk to each other and act on the world.
The more these protocols evolve, the more important it becomes to understand what they're for, and when to stop stretching them beyond their original scope. Because when used right, they open up a world where agents don't just chat; they get real work done.
Watch the full breakdown on our YouTube: https://www.youtube.com/watch?v=R0aYQZiH7RA
