A2A vs MCP: Are Google’s Agent2Agent and Anthropic’s Protocol Complementary?

Anthropic’s Model Context Protocol (MCP) has sparked interest across the AI industry, prompting other players to define open protocols for Agentic Systems integration. This week Google publicly released its Agent2Agent (A2A) protocol to standardize communication among multi‑agent systems, and many assume the two protocols are rivals rather than complements.

Google’s official stance is that A2A and MCP are complementary, a claim that appears reasonable, though it raises questions about long‑term competitive goals.

1. What is A2A?

A2A is an emerging open protocol that enables agents to collaborate without being constrained by underlying frameworks or vendors.

1.1 Problem

Future agent systems will be multi‑agent, often requiring remote collaboration where each agent may use different frameworks (e.g., LangGraph, CrewAI, Agent SDKs). Existing issues include:

Inability to transfer system state between agents built on different frameworks.

Impossible remote state transfer.

Disconnected agents do not share tools, context, or memory.

1.2 Solution

A2A provides a standard way for agents to cooperate, independent of the underlying stack.

According to Google’s documentation, A2A facilitates communication between “client” and “remote” agents. In simple terms, a client agent creates a task and hands it to a remote agent for execution or data return.

Main A2A capabilities include:

Ability discovery – agents expose an “Agent Card” describing their capabilities.

Task management – a protocol for handling short‑ and long‑term tasks, keeping agents synchronized until completion.

Collaboration – agents exchange context, replies, artifacts, or user instructions.

User‑experience negotiation – negotiates response formats (image, video, text, etc.) to match UI expectations.

Google suggests storing all Agent Cards in a unified location, e.g.:

https://<DOMAIN>/<agreed-path>/agent.json

The protocol builds on existing standards such as HTTP, SSE, and JSON‑RPC, making integration with enterprise IT stacks straightforward, and it includes default security comparable to OpenAPI authentication.

2. What is MCP?

Anthropic defines MCP (Model Context Protocol) as an open protocol that standardizes how applications provide context to large language models (LLMs).

An open protocol that standardizes how applications supply context to LLMs.

More precisely, MCP aims to standardize how LLM‑based applications integrate with other environments.

In agentic systems, context can be supplied via:

External data – part of long‑term memory.

Tools – capabilities to interact with the environment.

Dynamic prompts – injected as part of system prompts.

2.1 Why standardize?

Current agentic application development is fragmented: many frameworks with subtle differences, ad‑hoc integrations with external data sources, and inconsistent tool definitions. Standardization seeks to accelerate innovation, improve security, and simplify context injection.

High‑level MCP architecture:

MCP host – program using an LLM as core and accessing data via MCP.

MCP client – maintains a 1:1 connection with the server.

MCP server – lightweight program exposing specific functions through the protocol.

Local data source – files, databases, services accessible to the server.

Remote data source – external systems reachable via APIs.

2.2 Control responsibilities

MCP server exposes three main elements designed for isolation:

Prompt – controlled by the user; programmers expose specific prompts for LLM consumption.

Resource – controlled by the application; represents data (text or binary) used by the LLM, defined by AI engineers.

Tool – controlled by the model; MCP provides an endpoint listing available tools, their descriptions, and parameters, allowing the LLM to decide which tools to invoke for a given task.

3. A2A + MCP

Google’s official position states that agentic applications need both A2A and MCP: tool‑centric apps should use MCP, while agent‑centric apps should use A2A.

Agentic applications need A2A and MCP. We recommend tool‑based apps use MCP, agent‑based apps use A2A.

This combination means:

A2A handles secure collaboration, task and state management, and UX negotiation.

MCP provides standardized context, tool integration, and (now improved) authentication.

When combined, MCP hosts become agents themselves, and A2A enables their communication.

Key observations:

Open communication protocols will integrate new‑world agents.

Being a “lagging” protocol can still be advantageous.

Both protocols will continue evolving, potentially expanding their responsibilities.

3.1 Discovering agents via MCP

Google even suggests exposing A2A agents as MCP resources, allowing agents to discover each other through MCP‑provided Agent Cards and then communicate via A2A.

3.2 Will A2A eventually swallow MCP?

There is a risk that MCP may lose relevance as A2A gains traction, especially if global agent discovery indexes reduce the need for MCP‑based resource discovery.

3.3 Similarities and challenges

Both protocols aim to model agentic applications, but MCP faces issues such as limited security (though recent improvements address this) and lack of primitives for agent‑to‑agent communication.

3.4 Long‑term perspective

In the long run, companies may expose their data assets as agents, making the protocol that governs remote agent communication the true winner. If agents become the primary public interface, A2A’s focus on inter‑agent interaction could give it an edge.

4. Summary

We live in an exciting era where large‑scale agentic applications are being defined. A2A is rapidly emerging as a leader for cross‑agent communication, while MCP provides a structured way to integrate LLM context. Together they form a complementary stack that bridges model capabilities with system‑level collaboration.

Although the official stance is that the two protocols solve distinct problems, their scopes overlap and are likely to expand, making the combined ecosystem the most powerful foundation for future Agentic AI.