MCP vs A2A vs ACP: Key Differences Between AI Agent Protocols

Comparing Agent Protocols: MCP vs A2A vs ACP

As agent frameworks proliferate, developers are starting to ask important architectural questions: How should agents communicate? What protocols support those interactions? And where does MCP fit into this landscape?¹

This article provides a concise breakdown of the key agent communication protocols and what developers should consider when building AI-driven applications.

Two Protocol Families: Context vs Inter-Agent

Protocols in this space fall into two broad categories:

Context-Oriented Protocols (e.g. MCP): Focused on giving models structured access to tools, APIs, and memory.
Inter-Agent Protocols (e.g. A2A, ACP): Designed to enable autonomous agents to discover, authenticate, and collaborate with other agents over the network.

MCP – Lightweight, Practical, Widely Adopted

MCP (Model Context Protocol) is designed for simplicity. It allows language models to query a server for tools or structured memory and receive a valid response.¹

Why MCP matters:

Small and composable
Synchronous and fast by design
Broad adoption across frameworks
Easily extensible for real-world use cases

Tool call example:

{ "tool_name": "getWeather", "args": { "location": "New York" } }

Tool registry definition:

{ "tools": [ { "name": "getWeather", "description": "Fetches weather based on location", "parameters": { "location": "string" } } ] }

A2A – Asynchronous, Agent-Centric

A2A (Agent-to-Agent) is designed for agents that:

Discover each other
Communicate asynchronously
Negotiate and collaborate over time¹

Notable traits:

Embraces multi-turn, long-running interactions
Uses status polling and callbacks
Supports decentralized identity and verification

Async agent call example (pseudo-structured):

{ "agent_call": { "target": "agent://weather-bot", "task": "forecast", "parameters": { "city": "Paris" }, "callback_url": "https://myagent.ai/status" } }

ACP – Packaging and Remote Execution

ACP supports definitions for portable agents that can be downloaded and run in sandboxed environments.¹

Purpose:

Package agents with metadata
Enable registry-based discovery
Allow self-hosted or distributed execution

Agent manifest snippet:

{ "agent": { "name": "summarizer", "entrypoint": "main.py", "description": "Summarizes news articles", "permissions": ["read", "write"] } }

Summary Comparison

Feature	MCP	A2A	ACP
Purpose	Tool use	Agent collaboration	Agent definition + execution
Async support	Basic	Full	Partial
Discovery method	Manual / Registry	DID / Web URL	Registry-based
Hosting model	External	External	Self-hosted / Downloadable
Complexity	Low	Medium-High	Medium
Adoption	High	Early stage	Early stage

Key Takeaways

Start with MCP for synchronous LLM-to-server workflows.
Explore A2A when building collaborative or distributed multi-agent systems.
Watch ACP for developments in portable and secure agent execution.

MCP remains the most widely used today because it solves a narrowly scoped problem effectively—making it an excellent foundation for real-world applications.

Acknowledgements

This guide is based on Laurie Voss's² insightful presentation at the MCP Summit – "MCP vs ACP vs A2A: Comparing Agent Protocols"¹. His clear articulation of protocol design and the nuances between these frameworks shaped the structure of this comparative overview.

Special thanks to the LlamaIndex team and the broader agent protocol community for advancing the vision of secure, composable, and collaborative agent ecosystems.

MCP vs A2A vs ACP: Comparing Agent Protocols