MCP and A2A: The Two Protocols Defining How AI Agents Will Communicate

If you are building AI agents in 2026, two protocol acronyms matter more than any model benchmark: MCP (Model Context Protocol) and A2A (Agent-to-Agent). One solves tool integration. The other solves agent coordination. Together they are the infrastructure layer that turns isolated AI demos into production systems.

Here is what each one does, how they fit together, and what it means for your architecture decisions right now.

The Problem They Solve

AI agents are useful when they can use tools (query a database, call an API, execute code) and coordinate with other agents (delegate subtasks, receive results, chain workflows).

Before standard protocols, every team solved this differently:

• Tool schemas defined differently per model provider
• No standard for how agents discover each other
• No standard for task handoffs between agents
• Switching models meant rewriting integrations

MCP solves layer 1. A2A solves layer 2.

MCP: Standardizing Tool Use

Model Context Protocol (released by Anthropic, now adopted industry-wide) defines:

• A universal schema for describing tools (name, description, input schema, output schema)
• A standard transport (stdio or HTTP + Server-Sent Events)
• A runtime discovery protocol so models can find and call tools dynamically

How it works in practice

An MCP server is a process that exposes a list of tools. The model connects, queries the tool list, and calls tools by name with typed inputs.

# Simplified MCP server example (Python with fastmcp)
from fastmcp import FastMCP

mcp = FastMCP("Database Server")

@mcp.tool()
def query_crm(customer_id: str) -> dict:
    """Fetch customer record from CRM"""
    return crm_client.get(customer_id)

mcp.run()

Any MCP-compatible model can now use your query_crm tool without custom integration. Write once, use from any model.

Current adoption

• Anthropic Claude: native MCP support
• OpenAI: MCP support shipped
• Google Gemini: MCP support in progress
• Public MCP servers: GitHub, Stripe, Notion, Slack, dozens more

A2A: Standardizing Agent Coordination

Agent-to-Agent protocol (released by Google, April 2025) solves the harder problem: how do agents talk to each other?

A2A defines:

• Agent Cards: JSON descriptors served at /.well-known/agent.json describing what an agent can do
• Task messages: Structured requests and responses with explicit state tracking
• Streaming results: Long-running tasks stream partial results before completion
• Multi-turn interaction: Agents can ask clarifying questions mid-task

How it works in practice

{
  "name": "Research Agent",
  "description": "Searches and synthesizes information from web sources",
  "url": "https://agents.example.com/research",
  "capabilities": {
    "streaming": true
  },
  "skills": [
    {
      "id": "web-research",
      "name": "Web Research",
      "inputModes": ["text"],
      "outputModes": ["text", "file"]
    }
  ]
}

An orchestrating agent discovers this card, sends a task, and receives structured results. No custom handshake code needed.

How MCP and A2A Fit Together

They operate at different layers and compose cleanly:

Orchestration Layer (A2A: agent discovers, delegates, coordinates)
         |
Tool Use Layer (MCP: agent calls databases, APIs, browsers, code runners)

A research agent receives a task via A2A from an orchestrator. To complete it, the research agent uses MCP to call a web search tool and a document formatting tool. Results stream back via A2A.

The model is always the intelligence. MCP and A2A are the plumbing.

Architectural Implications

If you are building tool integrations: Build MCP servers. Your integration work is immediately compatible with any MCP-compatible model, now and as new models ship.

If you are building multi-agent workflows: Design around A2A from the start. The teams building agent systems that assume a single model or proprietary runtime are accumulating technical debt.

If you are building agent platforms: Expose Agent Cards at the standard endpoint. Make your platform composable with third-party agents.

The Open Problems

Two hard problems remain:

Authorization across agent boundaries: When agent A delegates to agent B, whose permissions apply? Agent-to-agent authorization is still being worked out.

Debugging multi-agent chains: A chain of five agents has five potential failure points. The tooling for tracing and observability in multi-agent systems is still early.

The Bottom Line

MCP and A2A are doing for AI agents what REST APIs did for web services: turning a fragmented landscape of custom integrations into a composable ecosystem.

Hundreds of MCP servers are publicly available today. A2A adoption is accelerating across enterprise platforms. Build on the standards now. The teams that understand the protocol layer they are building on will have the architectural leverage as the ecosystem matures.