MCP, ACP, A2A, Oh my!

The era of agents is here --- and with it, new protocols are emerging to power their interactions.

You may have encountered three recently: Model Context Protocol (MCP), Agent Communication Protocol (ACP), and Agent2Agent (A2A).

Each tackles a slightly different slice of the puzzle.

Let’s explore the three protocols, understand what they do, and highlight how they differ and complement one another.

Key protocol differences at a glance

Next, let’s look at each protocol more closely to determine what it’s intended for and excels at today.

Model Context Protocol (MCP)

MCP is an open protocol introduced by Anthropic focused on how LLM-based applications connect to data sources and tools.

Anthropic describes it as the “USB-C port for AI,” offering a standardized way to provide context and functionality to large language models.

Primary goal

Standardize how LLMs receive context (prompts, files, data streams) from diverse sources—local files, remote databases, or external services.

Core elements

Client-Server architecture

An MCP “host” (e.g., Claude Desktop, an IDE, or a custom AI tool) acts as the orchestrator and can connect to one or more “MCP Servers,” each exposing specific data or capabilities.

Resource & tool integrations

Each MCP Server can expose:

• Resources (e.g., static or queryable datasets like files or emails)
• Tools (e.g., invokable APIs or functions)
• Prompts (e.g., templated or dynamic context blocks)

These are then presented uniformly to the LLM.

When to use MCP

• If you primarily need to pipe data/tools to an LLM (e.g., giving it access to your local knowledge base or third-party services).
• If you want a single consistent protocol for hooking up lots of different LLM endpoints and expansions in a plug-and-play style.
• If you’re building secure or sandboxed LLM workflows that need to control access to tools or data.

Key Difference vs. A2A or ACP: MCP does not focus on multi-agent conversations or agent-to-agent negotiations. Instead, it focuses on hooking data and external tools into a single or multiple LLM-based processes.

Agent Communication Protocol (ACP)

ACP is IBM Research's agent-to-agent communication standard. It powers multi-agent workflows within BeeAI, an experimental platform that makes it easy to run and orchestrate open-source AI agents, regardless of the framework or code base.

Primary goal

Standardize how BeeAI’s agents talk to each other (and clients), removing the barriers posed by inconsistent agent interfaces.

Relationship to MCP

ACP originally drew inspiration from Anthropic’s MCP to hook agents to data/tools.

Today, ACP is evolving independently, introducing its own discovery, delegation, and multi-agent orchestration features.

Core elements

BeeAI Server

Orchestrates agent processes in a local-first environment, provides a single REST endpoint to external apps/UIs, and integrates with third-party frameworks.

Multiple agents

You can run open-source AI agents side by side, from code assistants to research bots, all connecting via ACP.

ACP SDKs

Python and TypeScript libraries, plus a CLI and UI for discovering and launching agents with minimal config.

Observability

Built-in telemetry and traceability (ties into tools like Arize Phoenix).

Pre-alpha stage and community focus

IBM is rallying the open-source community to help shape ACP’s future. Features like agent discovery, task delegation, and deep observability are still evolving.

BeeAI pivoted earlier this year to focus on developers, simplifying the process of finding, configuring, and running any open-source agent.

When to use ACP

• If you want a local-first approach—BeeAI runs on your machine or private infrastructure.
• If you need an easy way to spin up and orchestrate multiple agents, especially if they come from different frameworks and languages, behind a single platform.
• If you value deep telemetry and traceability for agent interactions.

Key Difference vs. A2A: While ACP can unify BeeAI’s internal multiagent environment, A2A is explicitly designed for bridging external agent frameworks and vendors (see below).

Reference: BeeAI + IBM Research.

Agent2Agent Protocol (A2A)

A2A is Google’s open protocol designed specifically for agent interoperability across frameworks—for example, hooking a LangChain-based agent up to another vendor’s agent.

Primary goal

Standardize multi-agent interactions so that agents from different frameworks can discover each other’s capabilities, exchange messages, and collaborate on tasks.

Core elements

Agent card

A public “manifest” (usually /.well-known/agent.json) describing an agent’s capabilities, endpoints, and auth requirements.

A2A server

The agent process that exposes an HTTP endpoint (implements the A2A spec). Receives requests, executes tasks, returns status/artifacts.

‍A2A client

‍Another agent or application that can issue tasks to the A2A Server.

‍Task lifecycle

‍A “task” is the fundamental work request that agents pass around, with states like submitted, working, input-required, completed, etc.

‍Streaming & push

‍Real-time updates with Server-Sent Events or push notifications for asynchronous workflows.

‍When to Use A2A‍

• If you have agents built in different frameworks but want them to seamlessly talk to each other or pass tasks back and forth.
• If you need robust multi-agent workflows where each agent can discover and invoke the skills of others.

Key Difference vs. MCP: A2A doesn’t address hooking up data sources or external context to a single LLM; it’s about bridging multiple agent frameworks.

Reference: A2A official Google GitHub README

Do they play well together?

It depends on your architecture and goals:

‍ACP + A2A

‍BeeAI’s ACP (currently pre-alpha) could, in theory, adopt A2A endpoints for external collaboration.

However, ACP’s current sweet spot is orchestrating agents within BeeAI. A2A is more direct if you want cross-framework communication outside the BeeAI ecosystem.

‍MCP + A2A

‍In a scenario where an LLM-based system uses MCP to gather relevant data, that same system could also present itself as an A2A agent. One protocol (MCP) feeds data; the other (A2A) integrates multiple agent ecosystems.

‍MCP + ACP

‍ACP initially built on MCP to hook up data and tools. Going forward, ACP aims to stand on its own, but there’s still potential for interoperability, especially if you want BeeAI’s local multi-agent environment while reusing your existing MCP-based data integrations.

Final thoughts

The AI ecosystem is evolving quickly, and these three agentic protocols—MCP, ACP, and A2A—reflect that diversity.

MCP is about standardizing how LLMs acquire context and access data; ACP, from IBM Research’s BeeAI project, orchestrates communication between multiple agents locally; and A2A ensures that agents from different frameworks can speak a common language.

By understanding each protocol’s core strengths, you can pick the right tool (or combination of tools) for your agent architecture. If you’re primarily focusing on data access for a single LLM, MCP might be the perfect fit.

If you want a local-first environment to run multiple open-source agents, ACP is a solid choice. If you have a cross-vendor agent environment, A2A can connect them.