A2A vs. MCP: What’s the difference and when should you use each?

Model Context Protocol (MCP) and Agent2Agent (A2A) solve different connection problems. MCP standardizes how an AI application or agent works with external tools, files, and data. A2A standardizes how independent agents discover one another, delegate work, and exchange results.

You can use either protocol on its own or combine them when several agents need external context and tools. Here’s a useful mental model to make the differences clear:

• MCP connects an agent to capabilities
• A2A connects an agent to another agent

Read on to learn more about the differences between MCP and A2A, and explore how they can be used together. Bring Dropbox into your AI workflow—explore AI workflows and connect the Dropbox remote MCP server to a supported MCP client. Choose a plan to get started today.

MCP vs. A2A at a glance

Take a look at what’s on the other end of the connection. If it’s a tool or a source of context, it leads to MCP. If it’s an independently operated agent, it leads to A2A.

What is MCP, and how does a remote MCP server work?

MCP uses a host-client-server structure. The host is the AI application. A client inside that host connects to an MCP server, which can expose resources for context, prompts for reusable instructions, and tools for defined actions.

A remote MCP server runs as a network service instead of on the host computer. A typical request follows four steps:

1. You connect the AI application to the MCP server and authorize Dropbox access.
2. The client learns which capabilities the server offers.
3. The application reads a resource or requests a tool action.
4. The server returns the result to the host.

For example, while working in Cursor, you could ask an assistant to find the latest product requirements in Dropbox. The Dropbox remote MCP server can search files you can access, return supported file content, and create a text-based decision log in the relevant folder. The agent gets useful project context without asking you to paste the same files into a prompt.

How is a remote MCP server different from an API, connector, or RAG system?

These terms describe different parts of the setup:

• API: An interface that software uses to request data or functionality. An MCP server may use APIs, while MCP gives compatible AI hosts a standard way to discover and call exposed capabilities.
• AI connector or plugin: A packaged product integration. The label describes the experience you add to a product, not necessarily its underlying protocol.
• RAG: An approach that retrieves external information and supplies it to a model during generation. MCP can provide retrieval for a RAG workflow, along with actions such as creating a file or shared link.

In short, APIs offer entry points, connectors organize how everything comes together, RAG finds the right details, and a remote MCP server ensures AI can easily locate and use both data and capabilities.

What is A2A, and how do agents work together?

A2A is an open standard for communication between independent agents. An agent can publish an Agent Card describing its endpoint, capabilities, skills, and authentication requirements. Another agent then uses that information to decide whether to delegate a task.

Imagine a release coordinator agent that needs an accessibility review. It discovers a specialist agent, sends the build details, and creates a task. The specialist can ask for missing information, report progress, and return a test report. The coordinator doesn’t need access to the specialist’s private memory or internal tools.

How can MCP and A2A work together?

Picture A2A as the horizontal connection between agents. MCP forms the vertical connection from each agent to the tools and context it needs.

Here’s an illustrative software-release workflow:

1. A coordinating agent receives the goal of preparing a release.
2. It uses A2A to delegate code review and release-note drafting.
3. Each specialist, through a configured MCP client, uses the Dropbox remote MCP server to find current requirements, read project notes, and create a text-based output in the correct folder.
4. The specialists return progress and completed work through A2A.
5. You review the results and keep the approved work with the rest of the project.

The same pattern can support other work. A campaign coordinator could delegate regional copy while MCP supplies the approved brief, current assets, and earlier feedback from Dropbox. On a construction project, a coordinator could assign a drawing comparison while the specialist gets the approved PDF plans and specifications. They then return the differences for human review.

These architectures are illustrative. In each example, the Dropbox remote MCP server supplies the MCP connection—the A2A layer would come from a separate system.

When should you use MCP, A2A, or both?

Use MCP when an AI application needs an external capability, such as finding a specification, reading a research file, creating a project note, or making a shared link.

Use A2A when one agent needs another agent to take responsibility for a larger piece of work, such as testing a release or obtaining a supplier quote.

Use both when agents collaborate and need their own tools or source material. A2A handles the handoff. MCP gives each agent a standard way to use the authorized tools and source material it needs.

Keep AI-assisted work connected with Dropbox

The Dropbox remote MCP server works with many supported AI clients. You can browse and search Dropbox, read supported file content, create folders and text-based files, make shared links, and more.

Connect Dropbox to a supported AI tool. Explore‌ AI workflows in Dropbox or choose a plan to bring project content into your workflow and give useful output a place to keep moving.