A2A and MCP: Detailed Comparison

In AI agent development, two kinds of protocols facilitate interoperability: one connects agents to tools and resources, the other enables agent-to-agent collaboration. The Agent2Agent (A2A) Protocol and the Model Context Protocol (MCP) address these distinct but highly complementary needs.

Horizontal and vertical layers

One way to picture the two protocols is by the direction they connect in.

MCP is vertical. It deepens a single agent. You can build an agentic system under one roof, with one or more agents in a single application. Every MCP connection you add gives an agent another tool, resource, or skill. The more you connect, the more that agent can do on its own.

A2A is horizontal. It connects agents across that boundary. The other agent may belong to another team, another department, or a partner organization. Your agent reaches out to those agents to discover them, negotiate, and exchange information.

That horizontal reach goes beyond asking another agent for a status update. It is closer to how one person reaches out to another for help: “Are you working on this project? Do you know about it? Can you share what you have, and if not, which agent can?” Agents break the ice, exchange context, and, depending on how the exchange goes, either go deeper or get pointed to a better place to get the job done.

Used together, MCP gives each agent depth, and A2A gives your system reach.

Model Context Protocol

MCP defines how an AI agent uses individual tools and resources, such as a database or an API. It offers these capabilities:

Standardizes how AI models and agents connect to and interact with tools, APIs, and other external resources.
Defines a structured way to describe tool capabilities, similar to function calling in large language models.
Passes inputs to tools and receives structured outputs.
Supports common use cases such as an LLM calling an external API, an agent querying a database, or an agent connecting to predefined functions.

Agent2Agent Protocol

A2A lets different agents collaborate to reach a common goal. It offers these capabilities:

Standardizes how independent, often opaque AI agents communicate and collaborate as peers.
Gives agents an application-level protocol to discover each other, negotiate interactions, manage shared tasks, and exchange conversational context and complex data.
Supports common use cases: for example, a customer service agent delegating an inquiry to a billing agent, or a travel agent coordinating with flight, hotel, and activity agents.

Why different protocols?

Both protocols are essential for building complex AI systems. The distinction depends on what an agent interacts with.

Tools and resources (MCP domain)
- Characteristics: typically primitives with well-defined, structured inputs and outputs that perform specific, often stateless functions, such as a calculator, a database query API, or a weather lookup service.
- Purpose: agents use tools to gather information and perform discrete functions.
Agents (A2A domain)
- Characteristics: more autonomous systems that reason, plan, and use multiple tools. They keep state over longer interactions and hold complex, often multi-turn dialogues to handle novel or evolving tasks.
- Purpose: agents collaborate with other agents to tackle broader, more complex goals.

A2A and MCP: complementary protocols for agentic systems

An agentic application might use A2A to communicate with other agents, while inside, each agent uses MCP to work with its own tools and resources.

An agentic application: the agent talks to black-box agents over the A2A protocol and reaches resources and tools through an MCP server

A2A connects the agents to each other; MCP connects each agent to its own tools.

Example scenario: the auto repair shop

Consider an auto repair shop staffed by autonomous AI agent “mechanics”. They diagnose and repair problems with special-purpose tools: vehicle diagnostic scanners, repair manuals, and platform lifts. The repair process can involve long conversations, research, and work with part suppliers.

Customer interaction: user-to-agent via A2A. A customer, or their assistant agent, talks to the “Shop Manager” agent. For example, the customer says, “My car is making a rattling noise.”
Diagnostic conversation: agent-to-agent via A2A. The Shop Manager agent holds a back-and-forth diagnostic conversation. For example, it might ask, “Can you send a video of the noise?” or “I see some fluid leaking. How long has this been happening?”
Internal tool use: agent-to-tool via MCP. The Mechanic agent, assigned the task by the Shop Manager, needs to diagnose the issue and uses MCP to reach its specialized tools. For example:
- MCP call to a “Vehicle Diagnostic Scanner” tool: scan_vehicle_for_error_codes(vehicle_id='XYZ123')
- MCP call to a “Repair Manual Database” tool: get_repair_procedure(error_code='P0300', vehicle_make='Toyota', vehicle_model='Camry')
- MCP call to a “Platform Lift” tool: raise_platform(height_meters=2)
Supplier interaction: agent-to-agent via A2A. The Mechanic agent finds that a specific part is needed and uses A2A to talk to a “Parts Supplier” agent and order it. For example, “Do you have part #12345 in stock for a Toyota Camry 2018?”
Order processing: agent-to-agent via A2A. The Parts Supplier agent is also an A2A-compliant system. It responds, which can lead to an order.

In this example:

A2A handles the higher-level, conversational, task-oriented interactions. It links the customer with the shop, and the shop’s agents with outside supplier agents.
MCP lets the mechanic agent use its specific, structured tools to perform its diagnostic and repair functions.

Representing A2A agents as MCP resources

An A2A server (a remote agent) can expose some skills as MCP-compatible resources. This works best when the skills are well defined and can be called in a tool-like, stateless way. Another agent might then discover the skill through an MCP-style tool description, perhaps derived from the Agent Card.

Still, A2A’s main strength is its support for flexible, stateful, collaborative interactions that go beyond a typical tool call. A2A is about agents partnering on tasks; MCP is more about agents using capabilities.

Use both together: A2A handles inter-agent collaboration and MCP handles tool integration, so you can build more powerful, flexible, and interoperable AI systems.