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MCP vs A2A — The 2 Protocols Every AI Developer Needs to Know

April 2026
12 min read
Deep Dive

Pick any AI engineering forum right now. Search "MCP vs A2A." You will find hundreds of threads where senior developers are confidently explaining these protocols — and getting them backwards, conflating them, or calling them competitors. They are not competitors. They are layers. And the sooner you understand that one sentence, the faster your multi-agent systems will actually work.

Six of the biggest companies in tech — OpenAI, Anthropic, Google, Microsoft, AWS, and Block — co-founded the Agentic AI Foundation (AAIF) under the Linux Foundation in December 2025. They put both protocols under the same neutral roof. That is not an accident. The industry is not picking a winner. The industry is saying these two protocols belong together.

This post gives you the full picture: what each protocol does, how the architecture works, what the code looks like, and exactly when to use which. By the end, you will never confuse them again.

Protocol Visual

MCP (Model Context Protocol)

Agent ↔ Tools & Data By Anthropic · Nov 2024 · Donated to AAIF Dec 2025

≠

A2A (Agent-to-Agent Protocol)

Agent ↔ Other Agents By Google · Apr 2025 · Donated to Linux Foundation Jun 2025

These protocols solve different problems. They work together, not against each other.

TL;DR

1. MCP = how an agent talks to tools, databases, and external APIs. Think USB-C for AI. 2. A2A = how an agent talks to other agents. Think HTTP for inter-agent communication. 3. Both now live under the Agentic AI Foundation (AAIF) — co-founded by OpenAI, Anthropic, Google, Microsoft, AWS, and Block. 4. In production multi-agent systems, you will almost certainly need both. MCP gives your agent hands; A2A gives your agent a workforce.

The Problem That Existed Before These Protocols

Before MCP existed, connecting an AI model to an external tool meant writing a bespoke plugin. Every tool — your SQL database, your GitHub API, your Slack integration — required its own custom adapter. If you switched AI frameworks, you rewrote every adapter. If you added a new model provider, you duplicated every integration. The engineering cost was enormous, and the result was still fragile.

The agent-to-agent problem was even messier. If you had a travel-planning agent and wanted it to work with a flight-booking agent built by a different team — or a different company — there was no standard way to do it. You negotiated formats, wrote custom bridges, and prayed neither team changed their API.

💡 Key Insight: Every protocol in the history of computing eventually gets standardized when the fragmentation tax becomes too expensive. MCP and A2A are the industry standardizing the agent-tool and agent-agent interfaces at the same time.

The analogy that actually works: think of a human employee. MCP is the employee's access to their tools — their laptop, their software, their database credentials. A2A is the employee's ability to email a colleague and ask them to handle part of a task. Both matter. They solve completely different things.

How We Got Here — A Quick Timeline

  1. 1.Nov 2024: Anthropic releases MCP (Open-sourced as a developer experiment).
  2. 2.Mar 2025: OpenAI adopts MCP across products. Once OpenAI aligned, Google and Microsoft followed quickly.
  3. 3.Apr 2025: Google launches A2A at Google Cloud Next.
  4. 4.Jun 2025: Google donates A2A to Linux Foundation.
  5. 5.Dec 2025: AAIF launched — both protocols under one roof. Over 100 enterprises joined by February 2026.

What Is MCP, Exactly? (And Why It Works)

MCP — the Model Context Protocol — is a standard that defines how AI models connect to external tools, data sources, and APIs. It follows a client-server architecture. The MCP client lives inside your AI application. The MCP server wraps whatever tool you want to expose — a database, a REST API, a local file system.

The "write once, use everywhere" promise is real and working. You build a Postgres MCP server today. That same server works with Claude, GPT-4o, Gemini, and Copilot — without rewriting anything. That is the USB-C analogy in practice.

How an MCP call actually flows

Here is what happens when a Claude agent uses an MCP tool to query a database. The agent decides it needs data, calls the MCP client, the client routes to the server, the server hits the database, and the result flows back. The model never touches the database directly.

# 1. Build an MCP server exposing a Postgres tool
from mcp.server import MCPServer
from mcp.types import Tool, ToolResult
import asyncpg

server = MCPServer("postgres-mcp")

@server.tool()
async def query_users(query: str) -> ToolResult:
    """Execute a read-only SQL query on the users table."""
    conn = await asyncpg.connect(dsn="postgres://localhost/mydb")
    rows = await conn.fetch(query)
    return ToolResult(content=str(rows))

# 2. The AI agent calls the tool via MCP client
# Agent decides: "I need to find users who signed up last week"
# MCP client routes the call -> server executes -> result returns to agent

⚠️ Common mistake: Treating MCP as a replacement for your existing API layer. It is not. MCP sits on top of your APIs — it gives AI models a standardized way to discover and call them. Your REST API stays. You just wrap it in an MCP server.

What Is A2A? (And What Problem It Actually Solves)

A2A — the Agent-to-Agent Protocol — is a standard for how AI agents from different vendors discover each other, delegate tasks, and coordinate on responses. It launched at Google Cloud Next in April 2025. It is specifically designed for one scenario: when your agent needs to hand work off to another agent.

The core concept in A2A is the Agent Card. Every A2A-compatible agent publishes a JSON metadata document at a predictable web address: /.well-known/agent-card.json. This card describes what the agent can do.

A concrete A2A scenario

Imagine you are building a travel-planning agent. A user says: "Book me a flight to Tokyo and find a hotel near Shibuya under $200/night." Your orchestrator agent cannot do both. Here is what the A2A flow looks like:

# 1. Orchestrator discovers the flight agent via its Agent Card
import httpx

async def discover_agent(base_url: str) -> dict:
    resp = await httpx.get(f"{base_url}/.well-known/agent-card.json")
    return resp.json()  # Returns: name, skills, auth_method, endpoint

# 2. Orchestrator delegates the flight task to the sub-agent
async def delegate_flight_task(agent_url: str, task: dict) -> dict:
    # A2A uses a standard Task object with status tracking
    payload = {
        "task_id": "task-tokyo-001",
        "skill": "book_flight",
        "input": {
            "destination": "Tokyo",
            "depart_date": "2025-08-15"
        }
    }
    resp = await httpx.post(f"{agent_url}/tasks", json=payload)
    return resp.json()

# 3. Sub-agent may respond asynchronously with status updates

Notice what A2A does not do. It does not tell the flight agent how to call the airline API. That is MCP's job. A2A handles the handoff — the "hey, you do this part" conversation. MCP handles the actual work inside each agent.

MCP vs A2A — Side by Side

  1. 1.Architectural layer: MCP (Agent ↔ Tool / Data Source) vs A2A (Agent ↔ Agent)
  2. 2.Created by: MCP (Anthropic, Nov 2024) vs A2A (Google, Apr 2025)
  3. 3.Transport: MCP (stdio, HTTP, SSE) vs A2A (HTTP, gRPC)
  4. 4.Best analogy: MCP (USB-C port for AI agents) vs A2A (HTTP for agents talking to agents)
  5. 5.Use when: MCP (Your agent needs to call a tool) vs A2A (Your agent needs to delegate work to another autonomous agent)

How MCP and A2A Work Together in a Real System

Here is where most explainers stop too early. Let us go one level deeper with a real production scenario: a customer support system with multiple specialized agents.

Architecture Flow: 1. A User Request travels to the central Orchestrator Agent. 2. The Orchestrator routes tasks using A2A to three sub-agents: Billing Agent, Order Agent, and Tech Support Agent. 3. Each sub-agent uses MCP to securely connect to external APIs and Tools: the Billing Agent hits the Stripe API, the Order Agent queries the Postgres DB, and the Tech Support Agent reads Zendesk/Jira.

Remove A2A: you have three isolated agents that cannot coordinate. Remove MCP: you have agents that talk to each other perfectly but cannot actually touch any real data. Both protocols are load-bearing.

Why the Agentic AI Foundation Changes Everything

The governance story matters as much as the technical story. Before December 2025, you had a legitimate concern: Anthropic controls MCP, Google controls A2A. What happens when their competitive interests diverge?

The AAIF answers that concern. Both protocols now sit under neutral Linux Foundation governance. Feature proposals go through open RFC processes. No single company can unilaterally change the spec for competitive advantage. Over 100 enterprises had joined as supporters by February 2026.

Conclusion

First: MCP and A2A are not in competition. They solve different problems at different layers. MCP gives your agent hands. A2A gives your agent a workforce. Every serious multi-agent system will use both.

Second: We are at the TCP/IP moment for autonomous agents. The three-layer stack — MCP for tools, A2A for agents, WebMCP for web access — is solidifying fast. The developers who understand this stack deeply in 2025 will be the architects of the systems that matter in 2027.

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