MCPとA2Aの違いとは？AIエージェントアーキテクチャにおける役割と使い分けの判断基準 #LLM - Qiita
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What Is the Difference Between MCP and A2A? Their Roles in AI Agent Architecture and Criteria for Choosing Between Them

  • AI
  • LLM
  • MCP
  • Architecture
  • System Design
0
Posted at 2026-11-12

Challenges Readers Face

When considering the introduction of AI agents and system integration, there is an increasing number of cases in which people struggle with the differences and how to choose between the concepts of "MCP (Model Context Protocol)" and "A2A (Agent-to-Agent) communication." If an architecture is decided while the boundary between the "standard specification for invoking tools and databases" and the "mechanism in which multiple autonomous agents cooperate with each other" remains ambiguous, there is a risk of reduced maintainability and excessive complexity.

What You Can Learn from This Article

  • The conceptual differences between MCP and A2A and their respective technical roles
  • Criteria and a flowchart for selecting the architecture in practice
  • Conceptual implementation examples of building an MCP server and integrating with A2A (including pseudocode)
  • Caveats at the time of introduction and a checklist for design

Target Readers and Prerequisites

  • Target readers: Engineers, architects, and IT practitioners who design systems that leverage AI agents
  • Prerequisite knowledge: Basic knowledge of communication protocols such as REST APIs and RPC, and an understanding of the basic concepts of Function Calling (tool invocation) with LLMs

Overview of MCP and A2A and Their Fundamental Differences

When considering the architecture of AI agents, first let us organize the definitions and purposes of both.

What Is MCP (Model Context Protocol)?

It is an open communication standard, proposed by Anthropic, that enables LLMs (models) to access external context (databases, files, APIs, etc.) through a standardized interface. It adopts a client-server model and serves to hide the specific implementation details of the connection destination from the LLM side.

What Is A2A (Agent-to-Agent)?

It is an architectural pattern and communication protocol that enables multiple independent AI agents, each with its own purpose and context, to perform dialogue, negotiation, and task delegation with one another.Rather than having a single model invoke tools, the aim is to enable “collaboration among entities that make decisions.”

Role Comparison Table

Comparison Item MCP (Model Context Protocol) A2A (Agent-to-Agent)
Primary Purpose Standardized access to resources and tools for models Coordination and task delegation among autonomous agents
Relationship Superior–subordinate relationship (Host/Client ↔ Server) Peer-to-peer or hierarchical inter-agent coordination
Nature of Communication Request–response (synchronous/procedural) Interactive/asynchronous/negotiation-based
State Management Primarily stateless (the Host manages the context) Each agent maintains its own internal state and context
Use Cases DB searches, reading local files, and invoking existing APIs Division of labor between planning agents and execution agents

Implementation Patterns and Examples

1. Example MCP Server Implementation (Python Pseudocode)

In the MCP context, it defines an interface for providing tools to models. The following is a standard conceptual example of registering tools.

# Example MCP Server Tool Definition (Conceptual Code) # Note: When operating this in practice, check the latest official SDK. from typing import Any, Dict class SystemMetricsMCPServer: def __init__self):self.tools = {} def register_tool(self, name: str, description: str, handler: callable): self.tools[name] = { "description": description, "handler": handler } def execute_tool(self, name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:if name not in self.tools: raise ValueError(f"Tool {name} not found") return self.tools[name]["handler"](**arguments) # Definition of the execution function (custom implementation example) def get_database_status(db_name: str) -> dict: # Perform the actual DB connection process (return dummy values here) return {"db_name": db_name, "status": "healthy", "active_connections": 5} # Server initialization and tool registrationserver = SystemMetricsMCPServer() server.register_tool( name="get_db_status", description"Gets the status of the specified database", handler=get_database_status )

2. Example of A2A (Agent-to-Agent) messaging (JSON message example)

In A2A integration, messages containing intent (Intent) and context are exchanged with each other, rather than merely passing the arguments of a function call.

{ "message_id": "msg-20260330-001", "sender_agent": "PlannerAgent", "receiver_agent": "ExecutionAgent", "intent": "REQUEST_TASK_EXECUTION", "payload": { "task_type": "DATA_ANALYSIS","constraints": { "max_time_seconds": 300, "required_output_format": "csv" }, "context_data": "Please analyze the access logs from the past week." }, "timestamp": "2026-03-30T10:00:00Z" }

Architecture Selection Flow and Decision Criteria

When building a system, this section organizes the criteria for deciding whether to adopt MCP or use an A2A architecture.

[Considering the processing to be implemented] | +---------------------+---------------------+ | | [Single model uses tools/DB] [Multiple specialized agents collaborate] | | 【Adopt MCP】 【Adopt A2A】 ・Standardized use of existing APIs ・Complex reasoning through role specialization ・Centralized context management ・Division of labor for asynchronous long-running tasks

Decision Criteria Checklist

Cases Where Adopting MCP Is Appropriate

  • You want the LLM to securely and in a standardized manner invoke the internal database or a specific web API
    • The operation being invoked is a well-defined function (request-response pair)
  • Negotiation among agents or autonomous feedback loops are not required
    • You want to centrally control and maintain context on the client side

Cases Where A2A Is Suitable

  • You want to combine agents with clearly differentiated roles and contexts, such as “planning,” “coding,” and “review”
  • You want to break down and process massive tasks that cannot be handled within a single prompt or context window
  • You want each agent to perform long-running asynchronous processing independently, then exchange and reconcile results upon completion
  • You want to use different permission settings and models (LLMs) for each agent

Considerations During Implementation and Operational Risks

  1. Increased Complexity Due to Excessive A2A Adoption
    If an A2A architecture is hastily adopted for problems that could be solved with a single agent plus MCP, it can introduce inter-agent loops, messaging overhead, and debugging difficulties. Start by considering the simplest configuration: extending tools with MCP.

  2. Security and Access Control
    In both MCP and A2A, there is a risk that agents may perform unexpected external operations. Make scope restrictions on callable APIs and built-in human approval through a “Human-in-the-Loop” mechanism mandatory in the design.

  3. Precautions Regarding Specification Updates
    Open standards and related SDKs for MCP and A2A are being updated rapidly. When implementing them, do not blindly rely on proprietary interface specifications; always check the official specifications and repositories.

Summary

  • MCPis a “standard plugin mechanism that connects LLMs with data and tools,” standardizing procedural access.
  • A2Ais a “protocol for communication and coordination among multiple autonomous agents,” enabling advanced division of labor and collaboration.

The two do not compete; a common practical pattern is a complementary layered architecture in which **“individual agents collaborate through A2A while each uses MCP locally to execute tools.”** Design appropriate boundaries according to your objectives.

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