MCP vs Google A2A Protocol: Complementary Standards Explained

MCP and Google's Agent-to-Agent (A2A) protocol solve fundamentally different problems: MCP standardizes how an AI model connects to tools and data sources, while A2A standardizes how autonomous AI agents communicate with each other. They are not competitors -- they are complementary layers of the emerging AI infrastructure stack, and many production systems will use both. Think of MCP as the protocol an agent uses to do things (read files, query databases, call APIs) and A2A as the protocol agents use to talk to each other (delegate tasks, share results, coordinate workflows).

This article explains the different problem domains each protocol addresses, compares their architectures, and shows how they work together in real-world systems.

Different Problems, Different Protocols

The simplest way to understand the MCP-versus-A2A distinction is to look at the direction of communication each protocol handles.

Dimension	MCP	Google A2A
Communication direction	Human/AI model to tools	Agent to agent
Primary relationship	Client-server (tool consumer to tool provider)	Peer-to-peer (agent to agent)
What gets exchanged	Tool calls, resource data, prompt templates	Tasks, messages, artifacts
Created by	Anthropic (November 2024)	Google (April 2025)
Core use case	Give an AI model access to external capabilities	Let multiple AI agents collaborate on complex tasks
Analogy	A worker using tools from a toolbox	Two specialists discussing how to divide a project

MCP is about capability access. An AI assistant needs to read a file, query a database, or create a pull request. MCP provides the standardized way to do that regardless of which AI model or host application is involved. For a full introduction, see our guide to MCP.

A2A is about task delegation. A travel-planning agent needs to coordinate with a flights agent, a hotels agent, and a payments agent -- each potentially built by a different organization, running different models, using different internal frameworks. A2A provides the standardized way for those agents to discover each other, exchange tasks, stream progress, and return results.

Architecture Comparison

MCP Architecture

MCP uses a host-client-server architecture:

Host: The AI application (Claude Desktop, Cursor, a custom app)
Client: A connector inside the host that manages a 1:1 connection to a server
Server: A program exposing tools, resources, and prompt templates

The communication is asymmetric. The client sends requests ("call this tool with these arguments") and the server responds with results. The AI model sits inside the host and decides when to invoke tools based on the conversation context.

Transports: stdio for local servers, Streamable HTTP for remote servers.

Message format: JSON-RPC 2.0 requests and responses.

For more on MCP's architecture, see our architecture deep dive.

A2A Architecture

A2A uses a client-server architecture at the transport level, but the relationship between agents is conceptually peer-to-peer:

A2A Client: An agent that initiates a task by sending it to another agent
A2A Server: An agent that receives, processes, and completes tasks
Agent Card: A JSON metadata file that describes an agent's capabilities, endpoint, and authentication requirements (similar to a business card)

The communication pattern revolves around tasks:

The client agent discovers a server agent via its Agent Card
The client sends a task (a structured request with a message)
The server agent processes the task, potentially streaming updates
The server returns artifacts (results) and a final task status

Transports: HTTP with JSON-RPC and Server-Sent Events for streaming.

Message format: JSON-RPC 2.0 (like MCP) with A2A-specific methods.

Key Architectural Differences

Component	MCP	A2A
Discovery	Client configured with server endpoint	Agent Card (JSON at well-known URL)
Session model	Persistent stateful sessions	Task-based (stateful or stateless)
Capability description	Tool schemas, resource URIs, prompt templates	Agent Card with skill descriptions
Streaming	SSE via Streamable HTTP	SSE for task progress updates
Authentication	OAuth 2.1 for remote servers	OAuth 2.0, API keys, or custom (defined in Agent Card)
Content format	Tool-specific JSON results	Structured "parts" (text, file, data)
Multi-turn	Supported via sampling	Supported via task message history

How They Complement Each Other

The real power emerges when you see MCP and A2A as different layers of the same stack.

Consider a complex enterprise workflow: a user asks an AI assistant to "prepare a quarterly business review presentation."

Layer 1 -- MCP (Tool Access): Each specialized agent uses MCP servers to access the tools it needs:

The data analysis agent connects to a PostgreSQL MCP server and a Google Sheets MCP server
The content generation agent connects to a filesystem MCP server and a web search MCP server
The design agent connects to a Figma MCP server and an image generation MCP server

Layer 2 -- A2A (Agent Coordination): The agents coordinate with each other via A2A:

The orchestrator agent sends a task to the data analysis agent: "Pull Q4 revenue data"
Once that completes, it sends a task to the content generation agent: "Write executive summary based on this data"
Finally, it sends a task to the design agent: "Create slides using this content"

In this architecture, MCP handles the vertical connections (agent to tools) and A2A handles the horizontal connections (agent to agent).

The Stack Visualized

Layer 3: User Interface
         |
Layer 2: Agent Orchestration (A2A)
         Orchestrator <-> Data Agent <-> Content Agent <-> Design Agent
         |                 |               |                |
Layer 1: Tool Access (MCP)
         |                 |               |                |
         v                 v               v                v
         Tools            Postgres         Filesystem       Figma
                          Sheets           Web Search       Image Gen

Feature-by-Feature Comparison

Feature	MCP	A2A
Open source	Yes (MIT license)	Yes (Apache 2.0)
Specification format	Formal spec document	Formal spec document
SDK languages	TypeScript, Python, Java, Kotlin, C#, Go	Python, TypeScript, Java (growing)
Industry adoption	Broad (Anthropic, OpenAI, Google, Microsoft, etc.)	Growing (Google, partners)
Handles tool invocation	Yes (primary purpose)	No (delegates to internal tooling or MCP)
Handles agent-to-agent	No (single client-server)	Yes (primary purpose)
Supports multimodal	Text and binary resources	Text, files, structured data, images
Push notifications	Supported in spec	Supported via SSE and webhooks
Enterprise readiness	Production-ready	Maturing rapidly

When to Use Each Protocol

Use MCP When...

An AI model or agent needs to interact with external tools, databases, APIs, or file systems
You want tool definitions to be reusable across multiple AI applications
You are building integrations between AI assistants and existing software systems
Your use case involves a single agent (or a single orchestrated pipeline) accessing capabilities
You want the broadest possible compatibility across AI vendors

Browse our MCP server directory to find servers for your use case.

Use A2A When...

You have multiple autonomous agents that need to collaborate on complex tasks
Agents are built by different teams or organizations and need a standard communication layer
You need to delegate subtasks from one agent to another with progress tracking
Your agents have different internal implementations (different models, frameworks, or languages)
You are building a marketplace or network of specialized AI agents

Use Both Together When...

You are building an enterprise AI platform with multiple specialized agents, each needing tool access
Your multi-agent system needs both inter-agent communication (A2A) and tool connectivity (MCP)
You want agents to discover each other (A2A Agent Cards) and discover tools (MCP capability negotiation)
You are designing a system where agents can be independently developed, deployed, and scaled

Common Misconceptions

"MCP and A2A are competitors." They are not. They address different communication patterns. Google explicitly positioned A2A as complementary to MCP when announcing the protocol. Many Google demonstrations show agents using MCP for tool access while using A2A for inter-agent communication.

"I need to choose one or the other." For simple applications with a single AI assistant using tools, you only need MCP. For multi-agent systems, you likely need both. Very few real-world architectures require A2A without also needing MCP.

"A2A replaces MCP for agent-based applications." A2A handles agent-to-agent communication, but agents still need to interact with non-agent tools and data sources. That is what MCP provides. A2A does not define how an agent reads a file or queries a database -- it defines how an agent asks another agent to do something.

"MCP cannot support multi-agent systems." MCP itself is a point-to-point protocol, but nothing prevents a multi-agent framework from giving each agent its own MCP client connections. The orchestration layer (whether A2A, LangGraph, CrewAI, or the OpenAI Agents SDK) coordinates between agents, while each agent independently uses MCP for tool access.

Protocol Maturity and Adoption

Metric	MCP	A2A
Launch date	November 2024	April 2025
Specification maturity	Stable, multiple versions	Early but well-specified
Number of implementations	Thousands of servers	Hundreds of agents (growing)
Major adopters	Anthropic, OpenAI, Google, Microsoft, Cursor, Replit	Google, Salesforce, SAP, various startups
Client/host support	Claude Desktop, Cursor, VS Code, ChatGPT, and many more	Google Agentspace, custom platforms
Community size	Very large	Growing rapidly

MCP has a significant head start in ecosystem maturity. A2A is newer but backed by Google and a growing coalition of enterprise partners. The two protocols are evolving in parallel, and we expect to see tighter integration points between them over time.

For details on how MCP's specification has evolved, see our MCP specification changelog.

The Future: A Unified Agent Infrastructure

The AI industry is converging on a layered architecture for agent systems:

Model layer: The LLMs themselves (Claude, GPT, Gemini, open-source models)
Tool layer (MCP): Standardized access to external capabilities
Agent communication layer (A2A): Standardized inter-agent collaboration
Orchestration layer: Frameworks for building agent workflows (Agents SDK, LangGraph, CrewAI, etc.)
Application layer: End-user products built on top of these layers

MCP and A2A together form the critical infrastructure layers (2 and 3) that make the rest of the stack possible. Investing in both protocols positions your AI applications for the multi-agent future that is rapidly approaching.