A2A and MCP: Detailed Comparison

In AI agent development, two key protocol types emerge to 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.

Model Context Protocol

The Model Context Protocol (MCP) defines how an AI agent interacts with and utilizes individual tools and resources, such as a database or an API.

This protocol offers the following 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

The Agent2Agent Protocol focuses on enabling different agents to collaborate with one another to achieve a common goal.

This protocol offers the following capabilities:

• Standardizes how independent, often opaque, AI agents communicate and collaborate as peers.
• Provides an application-level protocol for agents to discover each other, negotiate interactions, manage shared tasks, and exchange conversational context and complex data.
• Supports typical use cases, including 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 the MCP and A2A protocols are essential for building complex AI systems, and they address distinct but highly complementary needs. The distinction between A2A and MCP depends on what an agent interacts with.

• Tools and Resources (MCP Domain):
  • Characteristics: These are typically primitives with well-defined, structured inputs and outputs. They perform specific, often stateless, functions. Examples include 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: These are more autonomous systems. They reason, plan, use multiple tools, maintain state over longer interactions, and engage in complex, often multi-turn dialogues to achieve novel or evolving tasks.
  • Purpose: Agents collaborate with other agents to tackle broader, more complex goals.

A2A ❤️ MCP: Complementary Protocols for Agentic Systems

An agentic application might primarily use A2A to communicate with other agents. Each individual agent internally uses MCP to interact with its specific tools and resources.

An agentic application might use A2A to communicate with other agents, while each agent internally uses MCP to interact with its specific tools and resources.

Example Scenario: The Auto Repair Shop

Consider an auto repair shop staffed by autonomous AI agent "mechanics". These mechanics use special-purpose tools, such as vehicle diagnostic scanners, repair manuals, and platform lifts, to diagnose and repair problems. The repair process can involve extensive conversations, research, and interaction with part suppliers.

• Customer Interaction (User-to-Agent using A2A): A customer (or their primary assistant agent) uses A2A to communicate with the "Shop Manager" agent.

  For example, the customer might say, "My car is making a rattling noise".

• Multi-turn Diagnostic Conversation (Agent-to-Agent using A2A): The Shop Manager agent uses A2A for a multi-turn diagnostic conversation.

  For example, the Manager might ask, "Can you send a video of the noise?" or "I see some fluid leaking. How long has this been happening?".

• Internal Tool Usage (Agent-to-Tool using MCP): The Mechanic agent, assigned the task by the Shop Manager, needs to diagnose the issue. The Mechanic agent uses MCP to interact with 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 using A2A): The Mechanic agent determines that a specific part is needed. The Mechanic agent uses A2A to communicate with a "Parts Supplier" agent to order a part. For example, the Mechanic agent might ask, "Do you have part #12345 in stock for a Toyota Camry 2018?"

• Order processing (Agent-to-Agent using A2A): The Parts Supplier agent, which is also an A2A-compliant system, responds, potentially leading to an order.

In this example:

• A2A facilitates the higher-level, conversational, and task-oriented interactions between the customer and the shop, and between the shop's agents and external supplier agents.
• MCP enables the mechanic agent to use its specific, structured tools to perform its diagnostic and repair functions.

An A2A server could expose some of its skills as MCP-compatible resources. However, A2A's primary strength lies in its support for more flexible, stateful, and collaborative interactions. These interactions go beyond a typical tool invocation. A2A focuses on agents partnering on tasks, whereas MCP focuses on agents using capabilities.

Representing A2A Agents as MCP Resources

An A2A Server (a remote agent) could expose some of its skills as MCP-compatible resources, especially if those skills are well-defined and can be invoked in a more tool-like, stateless manner. In such a case, another agent might "discover" this A2A agent's specific skill through an MCP-style tool description (perhaps derived from its Agent Card).

However, the primary strength of A2A lies in its support for more flexible, stateful, and collaborative interactions that go beyond typical tool invocation. A2A is about agents partnering on tasks, while MCP is more about agents using capabilities.

By leveraging both A2A for inter-agent collaboration and MCP for tool integration, developers can build more powerful, flexible, and interoperable AI systems.