A2A vs MCP: The Difference Between AI Agent Protocols Explained

A2A vs MCP: The Difference Between AI Agent Protocols Explained

The difference between A2A and MCP comes down to who is talking to whom. MCP (Model Context Protocol) standardizes the connection between an agent and its tools and data, such as an ERP, a CRM or a document base. A2A (Agent2Agent) standardizes the conversation between independent agents, so that one can delegate work to another. In most companies, MCP comes first, and A2A only enters the picture when there are separate agents that need to cooperate.

Key takeaways

• MCP solves the vertical connection: agent to tool, agent to data source.
• A2A solves the horizontal connection: agent to agent, including agents from different teams or vendors.
• The two do not compete. The A2A documentation presents them as complementary.
• A single agent with good tools usually handles the process without any need for A2A.
• In both cases, the real risk lies in permissions, exposed data and missing decision logs, not in the protocol itself.

What MCP solves, according to the official documentation

The official introduction to the Model Context Protocol describes MCP as an open standard for connecting AI applications to external systems. The image used in the documentation is a standardized port: instead of writing a different integration for every pair of application and system, you expose the system once as an MCP server and any compatible client can use it.

In practice, an MCP server offers the agent three kinds of capability: tools (actions the agent can call), resources (data it can read) and prompts (reusable instruction templates). The agent, or the application that hosts it, acts as the client. Whoever writes the server decides what it exposes, and that is where company control comes in.

A simple example: a support agent needs to check the status of an order. With MCP, there is a server that exposes a read-only order lookup tool. The agent does not know the database. It only knows the tool, with its name, description and parameters. If you want to go deeper on the build side, read about how to build an MCP server for an internal system, starting with read-only access.

What A2A solves

The specification and documentation of the A2A protocol address a different problem: how an agent discovers, contacts and delegates tasks to another agent built by another team, on another framework or by another vendor. The agents do not need to share code, memory or a list of tools.

The core concepts are few:

• Agent Card: a document the agent publishes describing who it is, what it can do and how to authenticate with it.
• Task: the unit of delegated work, with its own lifecycle (started, in progress, waiting for input, completed or failed).
• Messages and artifacts: the exchange of context during the task and the results delivered at the end.

An important part of the proposal is opacity: the agent receiving the task does not have to reveal how it reasons, which tools it uses or what it keeps in memory. It only commits to an interface. This makes sense when the agents belong to different organizations, because nobody wants to expose their internal logic just to collaborate.

A2A relies on common web standards for transport and authentication, which brings it close to what integration teams already know. For the exact technical details (formats, methods, versions), the right source is always the specification, not third-party summaries, including this article.

What is the difference in practice?

A useful way to tell them apart is to ask what sits on the other side of the connection:

• If the other side is a deterministic function (look up, calculate, write), it is an MCP case.
• If the other side is something that decides how to do the work (an agent with its own reasoning, memory and tools), it is an A2A case.
• If the call should return quickly in a predictable format, it tends to be MCP.
• If the task may take a while, ask for clarification midway or deliver a composite result, it tends to be A2A.
• If you control both sides and the flow is fixed, you may need neither: a direct API call or a deterministic workflow is enough.

The last item deserves attention. Anthropic itself, in its guide Building effective agents, recommends starting with the simplest solution and adding complexity only when the gain is demonstrable. Multi-agent systems are a step above a single agent with tools, and that step has a cost in operations, debugging and security.

Illustrative scenario: when both show up together

Imagine a services company that handles customer support on WhatsApp. This is an illustrative scenario, not a real case.

The support agent receives a message from a customer who wants to reschedule a technical visit and dispute a charge. To reschedule, it uses a calendar tool exposed through MCP: it checks free slots and proposes options. This is a function with predictable input and output, so MCP fits well.

For the dispute, the business rules belong to the finance team, which maintains its own agent with access to billing, refund policy and approval limits, run by another team (or by a vendor). The support agent should not have those tools or know those rules. It delegates the task to the finance agent through A2A, which may reply asking for a document, or return an opinion ready for human review.

Note that the second part only requires A2A because there are two agents with different owners, permissions and maintenance cycles. If the same team maintained everything, an MCP tool for financial lookup with a handoff to a human might be simpler and safer. For the support side itself, the article on how to integrate AI into your company's WhatsApp covers the channel layer.

Does your company need both? A decision guide

Use the questions below, in order. Stop at the first one that settles your case.

• Does the process fit in a single agent with a few well-defined tools? Then start with tools, through MCP or a direct API, and do not adopt A2A.
• Do the systems the agent needs already have a trustworthy MCP server, or is it worth exposing them that way to reuse across several agents? Then adopt MCP.
• Is there a second agent maintained by another team or vendor, with rules and data you should not or cannot copy? Then evaluate A2A for that boundary.
• Can you explain in one sentence why splitting into two agents is better than giving one agent one more tool? If not, do not split.
• Is someone responsible for each agent, with a record of delegated tasks and a way to stop the flow? If not, the A2A conversation is premature.

Anyone using Claude Code or another agentic development environment is generally already in MCP territory. The guide on configuring MCP in Claude Code shows how configuration scopes separate what is personal, what belongs to the project and what belongs to the team, a precaution that applies to any MCP client.

What risks do the two protocols share?

Standardizing the connection makes integration easier, but it does not solve security. A few points apply to both:

• Permissions: every MCP tool and every A2A agent should have the least access possible. The OWASP authorization recommendations (deny by default, apply least privilege, verify on every request) apply directly.
• Trust in what comes from outside: a third-party MCP server or a remote agent can return malicious content or hidden instructions. Before installing, follow a checklist such as the one in how to audit an MCP server before installing it.
• Tool quality: vague descriptions lead to wrong calls. Anthropic covers this in its guide Writing effective tools for agents, with a focus on clear names, objective parameters and lean responses.
• Personal data: when one agent delegates a task to another, the data subject's information travels too. Under Brazil's LGPD, the company needs to know which data goes where, for what purpose and who is accountable for each step. That does not change just because the protocol is open.
• Traceability: every tool call and every delegated task should generate a record of who asked, what was done and what the result was. Without it, there is no way to investigate an error or prove what happened.

For controlling the input, output and actions of an agent, the article on guardrails for AI agents is a good complement.

How we apply the method: map, limit, pilot, hand over

To decide between MCP, A2A or neither, the path is the same as for any agent project. First, map the current process and record the baseline: who does the work, with which systems, where human decisions happen. Then limit data and actions: which tools the agent can call, what stays read-only, what requires approval. Next, pilot a real flow, with logs and a success metric agreed in advance. Finally, hand over the operation and decide whether to expand, adjust or stop.

In this approach, A2A rarely appears in the first pilot. It usually enters when the mapping reveals a real boundary between two owners, not as a starting point. To define the metric and decision criteria for the pilot, the guide on how to measure an AI automation pilot helps.

A five-step plan to assess the need

• List the systems and data sources the flow touches and mark which ones are read-only.
• List the decisions that require judgment and who owns each one today.
• Check whether any of those decisions belong to another team or vendor with its own rules. Each such boundary is a candidate for A2A.
• Solve what is a tool first, with MCP or an API, with minimal permissions and logging.
• Only then test a delegation between agents, in a small flow, with human review of the result.

Frequently asked questions

Does A2A replace MCP? No. They solve different problems: MCP connects an agent to tools and data, and A2A connects agents to each other. The A2A documentation treats the protocols as complementary, and the same agent can use MCP for its tools and A2A to talk to other agents.

Does my small company need A2A? Generally, no. If an agent with a few well-defined tools handles the flow, adding a second agent only increases maintenance cost and risk surface. A2A makes sense when agents maintained by different owners need to cooperate without exposing their internal logic.

Are MCP and A2A secure by default? No protocol guarantees security on its own. Security depends on authentication, minimal permissions, validation of what goes in and out, and logging of actions. Third-party servers and agents should be evaluated before use, like any integration that touches customer data.

Where should I start if I want to test agents in my company? Start by mapping one real process and choosing a small flow, with read-only tools and a success metric defined beforehand. If that pilot works and a clear boundary between two owners appears, then it is worth evaluating communication between agents. Before that, the protocol is an implementation detail, not the main decision.

Sources

• Model Context Protocol: introdução oficial
• Protocolo A2A: documentação oficial
• Anthropic: Building effective agents
• Anthropic: Writing effective tools for agents