ACP vs MCP: La guerra dei protocolli che definirà lo sviluppo IA nel 2026

The AI agent ecosystem now has two foundational protocols — one for tools, one for collaboration. Here's why developers need both.

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The AI agent revolution has a communication problem. As organizations deploy increasingly sophisticated AI systems, a critical gap has emerged: how do these agents talk to their tools — and to each other?

Two protocols are racing to become the foundational standards of the AI era. MCP (Model Context Protocol) by Anthropic handles the vertical connection — linking agents to tools, databases, and APIs. ACP (Agent Communication Protocol) by IBM Research handles the horizontal connection — enabling agents to communicate, collaborate, and delegate tasks to one another.

Together, they're building the communication stack for the next generation of AI systems. Think of it like this: MCP is the USB-C port that connects your device to peripherals. ACP is the Wi-Fi that connects devices to each other.

But which one do you need? When do you use both? And where does Google's A2A protocol fit in? Let's break it all down.

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What Is MCP (Model Context Protocol)?

MCP, created by Anthropic and released as an open standard in late 2024, has become the de facto way for AI agents to access external tools and data. In just six months after its release, it achieved widespread adoption across the AI ecosystem.

How MCP Works

MCP uses a client-server architecture with JSON-RPC for communication:

┌─────────────┐     JSON-RPC      ┌─────────────┐
│  AI Agent   │───────────────────▶│  MCP Server │
│  (Client)   │◀───────────────────│  (Tool)     │
└─────────────┘                    └─────────────┘
                                         │
                                         ▼
                                   ┌─────────────┐
                                   │  Database,  │
                                   │  API, File  │
                                   │  System     │
                                   └─────────────┘

Transport options:

• stdio — for local tool servers (fastest, simplest)
• Streamable HTTP — for remote servers (production-ready)
• SSE (Server-Sent Events) — for streaming responses

MCP Code Example

Here's a minimal MCP server that exposes a weather 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 response = await fetch(
      `https://api.weather.com/v1/current?city=${city}`
    );
    const data = await response.json();
    return {
      content: [
        {
          type: "text",
          text: `Weather in ${city}: ${data.temperature}°C, ${data.condition}`,
        },
      ],
    };
  }
);

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

Key MCP Features

MCP Strengths

• Massive ecosystem: Thousands of MCP servers already available for GitHub, Slack, databases, file systems, and more
• Simple integration: Any tool can become MCP-compatible with minimal code
• Universal adoption: Supported by Claude Code, Cursor, Windsurf, VS Code, and virtually every AI coding tool
• Battle-tested: MCP 1.0 shipped in early 2026 with a mature specification

MCP Limitations

• No agent-to-agent communication: MCP only connects an agent to tools — not to other agents
• Stateless: No built-in session persistence between calls
• Single-agent focus: Complex multi-agent workflows are outside its scope
• Manual discovery: Tools must be pre-configured; no automatic capability discovery

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What Is ACP (Agent Communication Protocol)?

ACP, developed by IBM Research and contributed to the Linux Foundation in March 2025, is designed to be the "HTTP of agent communication." It powers the open-source BeeAI platform for discovering, running, and composing AI agents.

How ACP Works

ACP uses a RESTful architecture over standard HTTP:

┌─────────────┐     REST/HTTP     ┌─────────────┐
│  Agent A    │───────────────────▶│  Agent B    │
│  (Triage)   │◀───────────────────│  (Service)  │
└─────────────┘                    └─────────────┘
       │                                  │
       │          REST/HTTP               │
       └──────────────────────────────────┘
              Peer-to-Peer Communication

Unlike MCP's client-server model, ACP enables peer-to-peer communication. Any agent can initiate a conversation or delegate a task to another agent — no boss required.

ACP Code Example

Here's how an agent registers and communicates via ACP:

from acp_sdk.server import Server
from acp_sdk.models import Message, MessagePart

server = Server()

@server.agent(
    name="research-agent",
    description="Researches topics using multiple sources",
    metadata={"capabilities": ["web-search", "summarization"]}
)
async def research_agent(input: list[Message], context):
    """Agent that researches a given topic."""
    topic = input[-1].parts[0].content
    
    # Perform research
    results = await search_multiple_sources(topic)
    summary = await summarize_findings(results)
    
    yield Message(
        parts=[MessagePart(content=summary)],
        role="agent"
    )

server.run(port=8000)

And here's how another agent calls it:

from acp_sdk.client import Client

async with Client(base_url="http://research-agent:8000") as client:
    # Discover available agents
    agents = await client.agents()
    
    # Start a conversation
    run = await client.run(
        agent=agents[0].name,
        input=[Message(parts=[MessagePart(
            content="Research the latest developments in quantum computing"
        )])]
    )
    
    # Get the result
    print(run.output[-1].parts[0].content)

Key ACP Features

ACP Strengths

• No SDK required: Interact with agents using curl, Postman, or any HTTP client
• Peer-to-peer: Agents communicate as equals — no central orchestrator needed
• Self-describing: Agents carry their own metadata for discovery, even in air-gapped environments
• Async-native: Built-in support for long-running tasks and asynchronous communication
• Open governance: Linux Foundation project with community-led development, no vendor lock-in

ACP Limitations

• Newer ecosystem: Fewer ready-made integrations compared to MCP
• No tool-calling standard: ACP focuses on agent communication, not tool access
• Early adoption: Still gaining traction outside the IBM/BeeAI ecosystem
• Less tooling support: Not yet integrated into major AI coding environments

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ACP vs MCP: The Head-to-Head Comparison

Here's the critical difference most developers miss: ACP and MCP are not competitors — they're complementary layers of the same stack.

When to Use MCP

Choose MCP when you need to:

• Connect an AI agent to databases, APIs, or file systems
• Build tool-augmented AI assistants
• Integrate AI into existing software infrastructure
• Access an ecosystem of pre-built connectors

When to Use ACP

Choose ACP when you need to:

• Multiple AI agents to collaborate on complex tasks
• Agents to delegate work to specialized peers
• Decentralized agent architectures without a central orchestrator
• Long-running async workflows between agents

When to Use Both

The real power emerges when you combine them:

┌───────────────────────────────────────────────────────┐
│                    ACP Layer                          │
│           (Agent-to-Agent Communication)              │
│                                                       │
│  ┌──────────┐    ACP     ┌──────────┐               │
│  │ Research │◄──────────▶│ Analysis │               │
│  │  Agent   │            │  Agent   │               │
│  └────┬─────┘            └────┬─────┘               │
│       │                       │                      │
│       │ MCP                   │ MCP                  │
│       │                       │                      │
│  ┌────▼─────┐            ┌────▼─────┐               │
│  │ Web API  │            │ Database │               │
│  │ Server   │            │ Server   │               │
│  └──────────┘            └──────────┘               │
│                                                       │
│                    MCP Layer                          │
│              (Agent-to-Tool Access)                   │
└───────────────────────────────────────────────────────┘

Real-world example: A financial analysis system where:

1. A Research Agent uses MCP to fetch market data from Bloomberg APIs
2. A Simulation Agent uses MCP to run Monte Carlo simulations via a compute cluster
3. Both agents use ACP to share results, compare findings, and produce a joint recommendation

As IBM's Kate Blair puts it: "Our goal is to build the HTTP of agent communication." And just as HTTP works alongside TCP/IP, ACP works alongside MCP.

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Where Does Google's A2A Fit In?

Google's Agent-to-Agent Protocol (A2A), unveiled shortly after ACP, takes a similar approach to agent-to-agent communication but with key differences:

Both ACP and A2A solve the agent-to-agent problem, and it's early enough that both may coexist. As Kate Blair says: "It's early days and I expect there will be a lot of iterations as protocols are put to the test under real-world use."

The emerging consensus: MCP for tools, ACP or A2A for agent collaboration. Which agent protocol wins may matter less than having one at all.

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The UTCP Wildcard

Worth mentioning: the Universal Tool Calling Protocol (UTCP) is emerging as yet another contender, attempting to unify tool-calling conventions across different model providers. While still early, UTCP aims to solve the fragmentation between OpenAI's function calling, Anthropic's tool use, and Google's tool declarations. Keep an eye on this space.

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Practical Setup Guide

Setting Up MCP (5 Minutes)

1. Install an MCP server:

npm install -g @modelcontextprotocol/server-filesystem

2. Configure your AI tool (e.g., Claude Code):

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/dir"]
    }
  }
}

3. Your AI agent can now read/write files through a standardized interface.

Setting Up ACP (10 Minutes)

1. Install the ACP SDK:

pip install acp-sdk

2. Create an agent:

from acp_sdk.server import Server

server = Server()

@server.agent(name="my-agent", description="My first ACP agent")
async def my_agent(input, context):
    yield Message(parts=[MessagePart(content="Hello from ACP!")])

server.run(port=8000)

3. Test with curl:

# Discover agents
curl http://localhost:8000/agents

# Send a message
curl -X POST http://localhost:8000/runs \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "my-agent", "input": [{"parts": [{"content": "Hello"}]}]}'

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What This Means for Developers in 2026

The protocol landscape is crystallizing around a clear pattern:

1. MCP is already essential. If you're building AI-powered applications, MCP support is table stakes. Claude Code 2.1, Cursor, Windsurf, and VS Code all support it. The ecosystem has thousands of servers.

2. ACP is the next wave. As single-agent systems evolve into multi-agent architectures, you'll need a way for agents to collaborate. ACP (or A2A) will become as important as MCP.

3. Learn both now. The Linux Foundation and DeepLearning.AI are launching ACP courses alongside existing MCP courses. Early adopters will have a significant advantage.

4. Build composable systems. Design your agents with both protocols in mind: MCP for tool access, ACP for inter-agent communication.

The "protocol war" framing makes for a good headline, but the reality is more collaborative than combative. MCP and ACP are solving different problems at different layers. The real winner is the developer ecosystem that finally gets standardized, interoperable AI communication.

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Frequently Asked Questions (FAQ)

Is ACP a replacement for MCP?

No. ACP and MCP are complementary protocols designed for different layers of the AI stack. MCP connects agents to tools and data (vertical integration), while ACP connects agents to each other (horizontal communication). Most production AI systems will use both.

Do I need ACP if I'm only building a single-agent system?

Probably not yet. If your application has one AI agent that needs to access tools and data, MCP alone is sufficient. However, as your system grows to include multiple specialized agents, ACP becomes valuable for coordinating their work.

Which protocol has better ecosystem support?

MCP has a significantly larger ecosystem as of early 2026, with thousands of pre-built servers and integration into all major AI development tools. ACP is newer but growing rapidly through the BeeAI platform and Linux Foundation backing.

Can I use MCP and ACP together?

Yes — this is the recommended approach for complex systems. Each agent uses MCP to connect to its tools and data sources, while ACP handles the communication between agents. The architecture diagram in this article illustrates this layered approach.

How do ACP and Google's A2A compare?

Both ACP and A2A solve the agent-to-agent communication problem with different approaches. ACP uses a pure REST/HTTP model with self-describing agents, while A2A uses Agent Cards for discovery and has structured task lifecycle management. Both are backed by major organizations (IBM/Linux Foundation for ACP, Google for A2A). It's too early to declare a winner.

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