MCP vs A2A Protocol: What's the Difference and When You Need Both

Your AI agent just called the wrong tool. Again. Or worse - it has no idea another agent even exists that could handle the task faster. If you're building agentic AI systems in 2025–2026, you've almost certainly bumped into two protocol names: MCP and A2A. They sound similar. They're not. And confusing them will cost you.

This post gives you the full picture: what each protocol does, where each one breaks down, and exactly when you need both - with a real-world example you can map to your own system.

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⚡ Key Takeaways

• MCP (Model Context Protocol) = agent ↔ tool. Vertical, centralized, built by Anthropic (2024).
• A2A (Agent-to-Agent Protocol) = agent ↔ agent. Horizontal, peer-to-peer, built by Google (April 2025).
• MCP alone can't orchestrate multi-agent workflows - it has no peer-to-peer mechanism.
• A2A alone can't give agents structured, permission-controlled access to tools and databases.
• Most production agentic systems need both - A2A routes the work, MCP does the work.
• Start with MCP. Add A2A the moment your system needs to delegate tasks between agents.

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What Is MCP (Model Context Protocol)?

MCP is an open standard that gives AI agents a clean, consistent way to connect to external tools and data. Think of it as the universal adapter that means you don't have to write custom glue code every time your agent needs to hit a new API or database.

Developed by Anthropic and open-sourced in November 2024, the model context protocol was built to solve a real frustration: every AI integration was a one-off. MCP fixes that with a single, stable contract. (For a full primer, see what is Model Context Protocol.)

Analogy: MCP is the USB-C port for AI - one standard connector, any tool.

How MCP works under the hood

The architecture is centralized and client-server. Your agent acts as the MCP client; it calls an MCP server that exposes tools, APIs, databases, and files. The server tells the agent exactly what's available via tool manifests - structured declarations of what tools exist and how to call them.

• Transport: JSON-RPC 2.0, with optional streaming
• Discovery: Tool manifests (the server advertises its capabilities)
• Security focus: Fine-grained permissions and access controls per tool

MCP is best for: single-agent apps, IDE plugins (VS Code, Cursor), chatbot assistants, and RAG pipelines that need structured access to business systems.

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What Is A2A (Agent-to-Agent Protocol)?

A2A is Google's open protocol for letting AI agents talk directly to each other - peer-to-peer, without a central controller brokering every conversation.

Google released A2A on April 9, 2025, with backing from 50+ technology and services partners including Atlassian, Salesforce, SAP, LangChain, Deloitte, and Accenture. The goal: make it possible for agents built by different vendors, on different frameworks, to find each other and collaborate without vendor lock-in.

Analogy: If MCP is the USB-C port, A2A is the Wi-Fi - devices find each other and collaborate without being plugged into the same hub.

How A2A works under the hood

The architecture is decentralized and peer-to-peer. Agents discover each other dynamically using three core concepts:

• Agent Cards - JSON documents each agent publishes to advertise its capabilities, supported tasks, and endpoints. This is how agents find each other.

• Tasks - the unit of work passed between agents, with a full lifecycle (created → in-progress → completed).

• Artifacts - the outputs a task produces (a report, a dataset, a decision).

• Transport: JSON-RPC + Agent Cards + Server-Sent Events (SSE) for streaming

• Security focus: Authentication, consent, and secure delegation between agents

A2A is best for: multi-agent workflows, cross-organization collaboration, autonomous pipelines where agents decompose and delegate tasks independently.

Google explicitly describes A2A as complementary to MCP, not a replacement for it.

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MCP vs A2A: Side-by-Side Comparison

The core difference is direction. MCP is vertical (agent ↔ tool). A2A is horizontal (agent ↔ agent). That single distinction explains almost everything else in the table below.

Feature	MCP	A2A
Created by	Anthropic (2024)	Google (2025)
Primary purpose	Connect agents to tools & data	Connect agents to other agents
Architecture	Centralized, client-server	Decentralized, peer-to-peer
Transport	JSON-RPC 2.0	JSON-RPC + Agent Cards + SSE
Discovery	Tool manifests	Agent Cards
Security focus	Tool permissions & access controls	Auth, consent, delegation
Complexity	Moderate	Higher
Best for	Single-agent tool access	Multi-agent orchestration

One-sentence summary: MCP is how an agent talks to its tools. A2A is how agents talk to each other.

The difference between A2A and MCP isn't about which is better - it's about which layer of your system you're solving for.

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Where Each Protocol Falls Short (And Why You Need Both)

Neither protocol is complete on its own. They solve different layers of the same problem. Here's exactly where each one hits its ceiling.

What MCP Can't Do Alone

MCP is excellent at connecting a single agent to its tools. But the moment your system grows beyond one agent, MCP runs out of road:

• No peer-to-peer negotiation between agents - it has no concept of one agent handing work to another.
• No support for independent multi-agent workflows - every interaction is mediated through a central server.
• No cross-organization agent discovery - MCP has no equivalent of Agent Cards for finding agents outside your own system.

Example: Imagine a research agent that needs to delegate a compliance check to a separate compliance agent. MCP has no mechanism for that handoff. The research agent can call its own tools all day - but it can't reach out and say "hey, compliance agent, take a look at this."

What A2A Can't Do Alone

A2A is excellent at routing work between agents. But routing isn't executing:

• No deep system integration - A2A doesn't define how an agent actually queries a database, calls an API, or reads a file.
• No long-running tool sessions - A2A manages task state between agents, not the fine-grained session state a tool connection needs.
• No fine-grained tool permissions - A2A handles agent-level auth, not tool-level access controls.

Example: A2A can route a task to a Data Agent - but that Data Agent still needs MCP to actually query the SQL database. A2A gets the work to the right agent; MCP gets the agent to the right data.

Together They Shine

When you stack them, the gaps disappear:

• A2A handles the orchestration layer - which agent does what, in what order, with what delegation.
• MCP handles the execution layer - how each agent actually uses its tools to get things done.

Think of it as a two-layer stack: A2A routes the work, MCP does the work.

This is why Google's own announcement described A2A as complementary to MCP, not a replacement. The protocols were designed to coexist. (For how this slots into the wider picture, see the three-layer AI agent stack.)

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Real-World Example: Enterprise Research Assistant

Let's make this concrete. Here's how A2A and MCP work together in a biotech company's AI research pipeline.

The task: "Summarize the latest findings on protein folding for drug discovery."

1. A meta-agent receives the task and breaks it into three workstreams.
2. It uses A2A to discover and delegate to three specialized agents via their Agent Cards:
   • A Literature Agent (finds and summarizes research papers)
   • A Data Agent (queries internal lab databases)
   • A Compliance Agent (checks findings against regulatory requirements)
3. Each sub-agent uses MCP to connect to its specific tools:
   • The Literature Agent uses MCP to call the PubMed API
   • The Data Agent uses MCP to query the internal SQL lab database
   • The Compliance Agent uses MCP to run against the compliance checklist system
4. Results flow back via A2A to the meta-agent, which synthesizes the final report.

Notice how A2A and MCP never compete - they operate at different layers. A2A never touches the PubMed API. MCP never knows the Compliance Agent exists. Each protocol does exactly one job, and does it well.

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When to Use MCP, A2A, or Both - Decision Guide

Use this simple framework to pick the right protocol (or both) for your system.

Use MCP if:

• You're building a single-agent app that needs to access tools, APIs, or databases
• You need standardized, permission-controlled tool access
• You're building an IDE plugin, chatbot assistant, or RAG pipeline
• Complexity is low and one agent can handle the full task

Use A2A if:

• Your workflow requires multiple specialized agents collaborating
• Tasks need to be decomposed and delegated across agents
• You need cross-organization or cross-platform agent discovery
• You're building autonomous pipelines where agents make decisions independently

(For a hands-on walkthrough of building multi-agent workflows, see our dedicated guide.)

Use both if:

• You're building enterprise-scale agentic AI systems
• Your system has both an orchestration layer AND an execution layer
• Any agent in your system needs to both coordinate with peers AND access external tools
• (Spoiler: this is most real-world production systems)

If you're unsure, start with MCP. Once your single-agent system needs to delegate work to another agent, add A2A. Don't over-engineer upfront - let your architecture tell you when you've outgrown a single protocol.

Related: How to Build Your First MCP Server in Python

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What About ACP? (A Quick Note)

There's a third protocol worth knowing: ACP (Agent Communication Protocol), developed by IBM's BeeAI team. ACP focuses on REST-based agent communication and was designed for local and embedded agent scenarios. In late 2025, IBM and the Linux Foundation announced that ACP would be merged into A2A under the Linux Foundation umbrella, winding down independent ACP development. For most teams today, MCP + A2A covers the main use cases. ACP is worth understanding historically, but A2A is now the convergence point for agent-to-agent communication standards.

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Key Takeaways

✅ MCP = agent ↔ tool (vertical, centralized, Anthropic, 2024)

✅ A2A = agent ↔ agent (horizontal, peer-to-peer, Google, April 2025)

✅ MCP alone can't orchestrate multi-agent workflows

✅ A2A alone can't give agents structured, permission-controlled tool access

✅ Most production agentic systems need both

✅ Start with MCP; add A2A when delegation becomes necessary

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FAQ

What is the difference between MCP and A2A?

MCP (Model Context Protocol) connects an AI agent to external tools, APIs, and data sources using a centralized client-server model. A2A (Agent-to-Agent Protocol) connects AI agents to each other using a decentralized, peer-to-peer model. The simplest way to remember it: MCP is vertical (agent ↔ tool); A2A is horizontal (agent ↔ agent). They solve different layers of the same agentic stack.

Can A2A replace MCP?

No - and it was never designed to. A2A handles agent-to-agent coordination, but it has no mechanism for structured tool access, long-running tool sessions, or fine-grained permissions. That's MCP's job. Using A2A without MCP means your agents can talk to each other but can't actually do anything useful with external systems.

Do I need both MCP and A2A?

Most single-agent systems only need MCP. But as soon as your system involves multiple agents delegating tasks to each other - a research agent handing off to a compliance agent, for example - you need A2A on top of MCP. Most production enterprise systems end up using both, with A2A handling orchestration and MCP handling execution.

Who created MCP and A2A?

MCP was created by Anthropic (specifically by engineers David Soria Parra and Justin Spahr-Summers) and open-sourced in November 2024. A2A was created by Google and released on April 9, 2025, with backing from 50+ industry partners including Salesforce, SAP, LangChain, Atlassian, and major consultancies like Deloitte and Accenture.

What are Agent Cards in A2A?

Agent Cards are JSON documents that each A2A-compliant agent publishes to advertise its capabilities, supported tasks, and endpoints. They're how agents discover each other dynamically - similar to how a service publishes an OpenAPI spec. When a client agent needs to delegate a task, it reads the Agent Cards of available agents to find the best match, then initiates communication via the A2A protocol.

Is A2A compatible with MCP?

Yes, by design. Google explicitly described A2A as complementary to MCP at launch. A2A handles the orchestration layer (which agent does what); MCP handles the execution layer (how each agent uses its tools). You can - and in most production systems, should - use both in the same agentic architecture. They operate at different layers and don't conflict.

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Useful Sources

• Anthropic - Model Context Protocol announcement
• Model Context Protocol - Official documentation
• Google Developers Blog - Announcing the Agent2Agent Protocol (A2A)
• A2A GitHub repository (specification)
• IBM Research - Agent Communication Protocol (ACP)
• Linux Foundation AI & Data - ACP joins forces with A2A