MCP vs A2A: Understanding the Two Protocols Defining AI Agent Architecture

Two open protocols are shaping how AI agents connect to the world and to each other. Anthropic's Model Context Protocol (MCP) defines how agents access tools and data. Google's Agent2Agent (A2A) protocol defines how agents talk to each other. They solve different problems, and understanding both is essential for anyone building agent systems in 2026.

The Problem Both Protocols Solve

Before MCP and A2A, every AI integration was custom. If you wanted an agent to read from a database, you wrote a custom connector. If you wanted two agents to collaborate, you hard-coded the communication logic. This created an N x M integration problem: N agents times M tools or services, each requiring its own glue code.

MCP and A2A standardize these interfaces. Think of them as contracts that both sides agree to, so any compliant agent can work with any compliant tool (MCP) or any other compliant agent (A2A).

MCP: Connecting Agents to Tools and Data

The Model Context Protocol, created by Anthropic, standardizes how an LLM-based agent discovers and uses external tools, reads data sources, and follows prompt templates.

How MCP Works

MCP follows a client-server architecture:

┌─────────────────┐     ┌──────────────┐     ┌──────────────────┐
│   AI Agent       │────▶│  MCP Client  │────▶│   MCP Server     │
│  (e.g. Claude)   │◀────│              │◀────│  (Tool Provider) │
└─────────────────┘     └──────────────┘     └──────────────────┘

The MCP Server exposes capabilities using three primitives:

• Tools: Functions the agent can call (search a database, send an email, run a query)
• Resources: Data the agent can read (files, database records, API responses)
• Prompts: Reusable prompt templates for common workflows

The MCP Client lives inside the host application (an IDE, a chatbot, a workflow engine) and handles the protocol communication.

A Minimal MCP Server

Here is a simple MCP server that exposes a weather lookup tool:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "weather-server",
  version: "1.0.0",
});

server.tool(
  "get_weather",
  "Get current weather for a city",
  { city: z.string().describe("City name") },
  async ({ city }) => {
    const data = await fetch(
      `https://api.weather.example/v1/current?city=${city}`
    );
    const weather = await data.json();
    return {
      content: [
        {
          type: "text",
          text: `${city}: ${weather.temp}°C, ${weather.condition}`,
        },
      ],
    };
  }
);

const transport = new StdioServerTransport();
await server.connect(transport);

The agent never needs to know the implementation details. It sees a tool called get_weather that takes a city name and returns weather data. The MCP protocol handles discovery, invocation, and response formatting.

MCP Adoption

MCP has grown rapidly since its release. Over 10,000 public MCP servers are active, and the protocol is integrated into ChatGPT, Cursor, GitHub Copilot, Microsoft Copilot Studio, AWS, Google Cloud, and Azure. The 2026 roadmap focuses on enterprise features: authentication, authorization, agent-to-agent communication, and governance controls.

A2A: Connecting Agents to Each Other

Google's Agent2Agent protocol solves a different problem. While MCP connects an agent to tools, A2A connects agents to other agents. When you have a travel-booking agent, a calendar agent, and an expense-reporting agent that need to collaborate on a business trip, A2A defines how they find each other, negotiate capabilities, and exchange work.

How A2A Works

A2A uses three core concepts:

Agent Cards: JSON metadata files (typically at /.well-known/agent.json) that describe what an agent can do. Other agents read this card to decide whether to delegate work.

{
  "name": "expense-reporter",
  "description": "Processes expense reports and receipt categorization",
  "url": "https://agents.example.com/expense",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "id": "categorize-receipt",
      "description": "Categorize a receipt image into expense categories"
    },
    {
      "id": "generate-report",
      "description": "Generate a formatted expense report from categorized items"
    }
  ]
}

Tasks: The unit of work in A2A. One agent creates a task and sends it to another agent. Tasks have states (submitted, working, completed, failed) and can include artifacts (files, structured data).

Messages: The communication within a task. Agents exchange messages that contain parts (text, files, structured data). This supports both synchronous request-response and asynchronous streaming patterns.

A2A Communication Flow

Agent A                          Agent B
   │                                │
   │  1. Discover (read Agent Card) │
   │───────────────────────────────▶│
   │                                │
   │  2. Create Task                │
   │───────────────────────────────▶│
   │                                │
   │  3. Status: "working"          │
   │◀───────────────────────────────│
   │                                │
   │  4. Status: "completed"        │
   │     + Artifact (result)        │
   │◀───────────────────────────────│

A2A is transport-agnostic (HTTP, WebSocket, gRPC) and model-agnostic (agents can run on any LLM or no LLM at all). The protocol is now governed by the Linux Foundation with backing from over 50 partners including Salesforce, SAP, Atlassian, and PayPal.

MCP vs A2A: Key Differences

Aspect	MCP	A2A
Primary purpose	Connect agent to tools and data	Connect agent to other agents
Architecture	Client-server	Peer-to-peer
Created by	Anthropic	Google
Discovery	Server capabilities list	Agent Cards (.well-known/agent.json)
Unit of work	Tool call (synchronous)	Task (can be long-running)
Communication	Structured tool I/O	Rich messages with multiple parts
Governance	Anthropic-led (open spec)	Linux Foundation
Best for	Single agent using external tools	Multi-agent collaboration

They Are Complementary, Not Competing

This is the most important point: MCP and A2A operate at different layers of the stack.

┌────────────────────────────────────────────┐
│           Multi-Agent System               │
│                                            │
│  ┌──────────┐  A2A   ┌──────────┐        │
│  │ Agent A   │◀──────▶│ Agent B   │        │
│  └────┬─────┘        └────┬─────┘        │
│       │ MCP                │ MCP           │
│  ┌────▼─────┐        ┌────▼─────┐        │
│  │ Database  │        │ Calendar  │        │
│  │ Server    │        │ Server    │        │
│  └──────────┘        └──────────┘        │
└────────────────────────────────────────────┘

Agent A uses MCP to query a database. Agent B uses MCP to read a calendar. Agents A and B use A2A to coordinate with each other. A production system will likely use both.

When to Use Each Protocol

Use MCP when:

• Your agent needs to access external tools (APIs, databases, file systems)
• You are building integrations for an existing agent platform
• You want a single agent to have broad capabilities without custom code per tool
• You need standardized tool discovery and invocation

Use A2A when:

• Multiple specialized agents need to collaborate on complex tasks
• You are building a marketplace or ecosystem of agent services
• Tasks are long-running and require asynchronous status updates
• Different teams or organizations build agents that must interoperate

Use both when:

• You are building a production multi-agent system (most real-world cases)
• Individual agents need tool access (MCP) AND need to delegate to other agents (A2A)

Building with Both Protocols

Here is a sketch of an agent that uses MCP for tool access and A2A for delegation:

class TravelAgent:
    def __init__(self):
        # MCP: connect to tools
        self.flight_tool = MCPClient("flight-search-server")
        self.hotel_tool = MCPClient("hotel-search-server")

        # A2A: discover peer agents
        self.expense_agent = A2AClient(
            "https://agents.internal/expense/.well-known/agent.json"
        )

    async def plan_trip(self, request):
        # Use MCP tools directly
        flights = await self.flight_tool.call(
            "search_flights",
            {"origin": request.origin, "dest": request.dest}
        )
        hotels = await self.hotel_tool.call(
            "search_hotels",
            {"city": request.dest, "dates": request.dates}
        )

        # Delegate to another agent via A2A
        expense_task = await self.expense_agent.create_task({
            "skill": "generate-report",
            "data": {"flights": flights, "hotels": hotels}
        })

        # A2A task is async, poll for completion
        result = await expense_task.wait_for_completion()
        return result.artifacts

Security Considerations

Both protocols are adding enterprise-grade security features:

MCP is implementing OAuth 2.1 for server authentication, scoped permissions for tool access, and audit logging. The 2026 roadmap emphasizes governance: who can register servers, what data they can access, and how to revoke access.

A2A includes authentication via Agent Cards, encrypted message transport, and capability-based access control. Since agents can delegate to other agents, the trust chain becomes critical. You need to verify that Agent B is authorized before sending it sensitive data.

The key principle: never trust an agent's self-description alone. Validate capabilities, authenticate identities, and scope permissions at every boundary.

What Comes Next

The agent protocol landscape is still evolving. MCP is expanding toward agent-to-agent communication (potentially overlapping with A2A). A2A is considering tool-use standardization (potentially overlapping with MCP). Google has even announced an AI Agent Marketplace for buying and selling A2A-compatible agent services.

The likely outcome is convergence or interoperability between the two protocols. For now, the practical approach is to learn both, understand their strengths, and use each where it fits.

Key Takeaways

• MCP standardizes how agents connect to tools and data (agent-to-tool)
• A2A standardizes how agents communicate with each other (agent-to-agent)
• They solve different problems and are designed to be complementary
• MCP has broader adoption today (10,000+ servers, integrated in major platforms)
• A2A has strong enterprise backing (50+ partners, Linux Foundation governance)
• Production agent systems will typically use both protocols
• Security and governance are the next frontier for both

Understanding these protocols is not optional for engineers building AI systems. They are becoming the standard infrastructure layer for agent-based applications, similar to how HTTP became the standard for web communication.

Practice building agent systems with both protocols on ByteMentor's Agent Builder lab.