AI agents are getting more capable, but connecting them is getting more confusing.
Two protocols keep appearing in that conversation: MCP (Model Context Protocol) and A2A (Agent2Agent Protocol).
At first glance, they can sound like competing standards. They are not.
MCP standardizes how an AI application accesses tools, resources, and external capabilities. A2A standardizes how independent agents discover and communicate with one another.
A simple mental model is:
MCP: Agent → Capability
A2A: Agent → Agent
If your coding agent needs to query a repository, read a database, or invoke an internal service, MCP may be the relevant boundary.
If your travel agent needs to delegate hotel search to a separate hotel agent owned by another service, A2A is closer to the problem.
And yes, a system can use both. Google Cloud already demonstrates agent architectures in which MCP and A2A operate together rather than as mutually exclusive standards.
That distinction matters because architecture decisions go wrong when developers compare MCP and A2A as though only one can exist in a stack.
Key Takeaway: MCP and A2A solve different interoperability problems. MCP connects AI applications to capabilities. A2A connects autonomous agents to other autonomous agents.
MCP vs A2A at a Glance
| Area | MCP | A2A |
|---|---|---|
| Full name | Model Context Protocol | Agent2Agent Protocol |
| Main problem | Access to tools, resources, and capabilities | Communication between independent agents |
| Typical boundary | AI app/client ↔ MCP server | Agent ↔ agent |
| Core interaction | Discover and invoke capabilities | Send messages and delegate tasks |
| Discovery | Server-exposed capabilities | Agent Cards |
| Best fit | Tool and data integration | Cross-agent collaboration |
| Can they work together? | Yes | Yes |
That table is the short answer.
The rest of the article explains why the distinction matters in real systems.
Why Do We Need Two Protocols?
The first generation of LLM applications often looked like this:
User → Prompt → LLM → Response
Modern agent systems look less tidy.
User
↓
Agent
├── Database
├── Search
├── Internal API
├── Payment service
└── Another agent
These connections are not all the same.
A database query is a capability call.
A remote agent, on the other hand, may decide for itself how to complete a delegated goal. It might use its own model, tools, memory, policies, and workflow.
That creates two separate interoperability boundaries:
Agent ↔ Capability
Agent ↔ Agent
MCP primarily addresses the first. A2A addresses the second. Google’s developer guidance similarly positions MCP around tools and data while A2A handles communication between agents.
This is also where context engineering in modern AI systems becomes relevant: reliable agents depend not only on prompts, but on how tools, data, state, and other actors are exposed to the model.
What Is MCP?
The Model Context Protocol is an open protocol for connecting AI applications with external context and capabilities.
Its architecture follows a host/client/server model. An AI application acts as the host, creates MCP clients, and connects those clients to MCP servers. Servers then expose capabilities that the application can discover and use.
The official Model Context Protocol architecture documentation defines core server primitives including:
- Tools — executable functions
- Resources — contextual data
- Prompts — reusable interaction templates
[Source: Model Context Protocol Documentation, 2026]
A basic architecture looks like this:
AI Application
↓
MCP Client
↓
MCP Server
↓
Tool / Data / Service
Suppose you are building a support agent.
It needs access to order data. Instead of hard-wiring custom integration logic into every AI application, an MCP server could expose capabilities such as:
get_order
get_customer
lookup_shipment
issue_refund
The application can discover those capabilities and invoke the appropriate one.
A Practical MCP Example
Imagine a coding assistant that needs repository information:
Coding Agent
↓
MCP
↓
Repository Server
↓
Git Repository
The important point is that the repository server is not necessarily another autonomous agent.
It exposes capabilities.
The coding agent still owns the reasoning loop: decide what information is needed, invoke the relevant capability, inspect the result, and choose what to do next.
A 2026 MCP Detail Worth Knowing
Be careful with older MCP diagrams.
The MCP 2026-07-28 specification update moved MCP's protocol core from a stateful session model to a stateless request/response model. The revision removed the old protocol-level handshake and session requirement while adding changes intended to make MCP workloads easier to route, cache, secure, and scale.
[Source: Model Context Protocol, 2026]
Your application can still maintain state.
The key change is that the core protocol itself no longer requires hidden transport session state.
For beginners, you do not need to memorize the migration details. Just avoid assuming every current MCP interaction depends on the older session model.
What Is A2A?
A2A, or Agent2Agent Protocol, is designed for communication between independent agents.
The current Agent2Agent protocol specification defines operations for sending messages, handling tasks, streaming updates, and discovering agents.
[Source: A2A Protocol Specification, 2026]
The simplest architecture is:
Agent A
↓
A2A
↓
Agent B
But the important word here is agent.
Agent B is not just a function such as:
get_weather("Boston")
It may own a full workflow.
You could send it a goal such as:
Find a hotel near the conference venue
for under $250 per night, with Wi-Fi,
for September 10–12.
The remote hotel agent may decide:
- which inventory sources to query,
- which filters to apply,
- whether clarification is needed,
- how to rank the options,
- and what result to send back.
The calling agent delegates the goal, not every internal step.
What Are Agent Cards?
A2A needs a way for agents to describe themselves.
That is where the Agent Card comes in.
The A2A specification describes an Agent Card as a self-describing manifest carrying information such as an agent's identity, capabilities, skills, supported interfaces, version, and security requirements.
[Source: A2A Protocol Specification, 2026]
Conceptually:
Hotel Agent
├── capability: hotel_search
├── capability: availability_check
├── interface: A2A
└── authentication: ...
A client can inspect that information before deciding whether the remote agent is appropriate for a task.
That is different from simply knowing that an HTTP endpoint exists.
The Difference That Actually Matters: Tool vs Agent
This is the part worth remembering.
MCP: Give an Agent a Capability
Agent → Tool
The calling agent usually remains responsible for deciding how the capability fits into the larger task.
Example:
calculate_shipping(order_id)
The tool performs a defined operation and returns a result.
A2A: Give an Agent an Autonomous Collaborator
Agent → Agent
The remote agent owns more of the problem-solving process.
Example:
Plan the lowest-cost shipping strategy
for these 200 orders while meeting
our delivery SLAs.
That agent might call multiple services, evaluate constraints, retry failed actions, or ask for more information.
So here is a useful boundary:
A tool exposes a capability. An agent owns how a goal gets solved.
That is not a perfect philosophical definition of an AI agent.
It is, however, a very useful architecture test.
Key Takeaway: If the remote system exposes a capability, think MCP. If it owns reasoning and accepts delegated goals, A2A becomes more relevant.
MCP and A2A Are Not Competitors
Now we can combine both ideas.
Consider a travel platform:
User
↓
Travel Agent
│
├── MCP → Calendar
│
├── MCP → Customer Database
│
└── A2A → Hotel Agent
│
├── MCP → Hotel Inventory
└── MCP → Maps Service
The travel agent uses MCP to access capabilities it needs directly.
But instead of implementing hotel-search reasoning itself, it delegates that goal to a specialized hotel agent over A2A.
The hotel agent can then use its own MCP-connected capabilities.
Google Cloud has published hands-on material on building connected agents with MCP and A2A, demonstrating both standards inside the same agent stack.
[Source: Google Cloud, 2025]
This is the architecture many surface-level comparisons miss.
The question is not:
Which protocol wins?
The better question is:
Which boundary am I standardizing?
When Should You Use MCP?
MCP makes sense when an AI application needs standardized access to tools, resources, or services.
Consider it when:
Your agent needs external capabilities.
Databases, SaaS systems, search, file systems, internal APIs, developer tools, and similar resources are natural candidates.The integration should be reusable.
If several AI clients may need the same capability, a standard interface can reduce one-off integration work.Discovery matters.
MCP clients can discover server-exposed tools and other primitives instead of assuming every capability is statically wired.The capability provider and AI application evolve independently.
A protocol boundary can help keep those concerns separate.
When MCP May Be Unnecessary
Do not add MCP merely because your agent calls a function.
If one application owns both sides and all you need is:
result = calculate_tax(order)
a normal function call may be simpler.
Protocols add value at boundaries.
They also add architecture.
Use that architecture when interoperability, reuse, or independent ownership actually justifies it.
When Should You Use A2A?
A2A becomes more interesting when the remote participant is itself an independent agent.
Use it when:
- One agent needs to delegate work to another.
- Agents live in different services or runtimes.
- Different teams or organizations own the agents.
- Framework or language independence matters.
- The remote agent should hide its internal tools and workflow.
- Agent capability discovery matters.
- Tasks may be long-running or require multiple messages.
The A2A specification supports direct responses as well as task-oriented interactions that may continue asynchronously, making it suitable for work that cannot always be represented as a single immediate function response.
[Source: A2A Protocol Specification, 2026]
When A2A May Be Overkill
Suppose you have two agents inside the same Python process:
PlannerAgent → WriterAgent
Both use the same framework.
Both ship together.
Neither needs to be independently discoverable.
You probably do not need a network interoperability protocol just to let them communicate.
Your framework's native agent composition may be enough.
A2A becomes more valuable as the boundary becomes real: separate service, separate runtime, separate framework, separate vendor, or separate owner.
A Simple Decision Framework
When you are unsure, run through these four steps.
Step 1: Identify What You Are Connecting
Is the remote side primarily a tool, data source, or capability?
Consider MCP.
Is it an independent agent capable of owning a delegated goal?
Consider A2A.
Step 2: Ask Who Owns the Execution Logic
If your agent decides every step and the remote side executes a defined operation, MCP or ordinary tool calling is likely closer to the problem.
If the remote side decides how to accomplish the requested goal, A2A is more relevant.
Step 3: Find the Interoperability Boundary
Same process and same codebase?
A protocol may be unnecessary.
Cross-service, framework, organization, or vendor?
A standardized protocol becomes much more valuable.
Step 4: Check Whether Both Boundaries Exist
Many real systems have both:
Agent → Agent → Tools
A2A MCP
In that case, using MCP and A2A together is completely reasonable.
Common MCP vs A2A Misconceptions
“A2A replaces MCP.”
No.
They primarily target different relationships.
“MCP is just for tool calling.”
That is too narrow.
MCP servers can expose tools, resources, and prompts, so its scope goes beyond function invocation alone.
“Every multi-agent application needs A2A.”
No.
Internal agents can communicate through framework-native mechanisms when there is no meaningful interoperability boundary.
“If I use A2A, the remote agent cannot use MCP.”
It can.
An A2A-connected agent can internally use MCP servers to access its own capabilities.
What Should Developers Learn First?
If you are new to agent protocols, I would learn them in this order:
1. LLM tool calling
↓
2. MCP basics
↓
3. Single-agent workflows
↓
4. Multi-agent architecture
↓
5. A2A
Why this order?
MCP is easier to understand once you already know why an LLM calls tools.
A2A becomes easier once you have built an agent that owns a reasoning loop and you can clearly distinguish that agent from a normal tool.
Do not start by memorizing protocol payloads.
Start by learning the architectural boundaries.
MCP vs A2A Cheat Sheet
Use MCP: Agent → Tool / Resource / Capability
Use A2A: Agent → Agent
Use both: Agent → Agent → Tools
That is the core idea.
MCP gives AI applications a standardized way to interact with external capabilities. A2A gives independently built agents a standardized way to discover one another, exchange messages, and delegate work.
Neither protocol automatically makes an agent intelligent.
Neither removes the need for good architecture.
And neither needs to “beat” the other.
When you are designing an agent system, stop asking:
“MCP or A2A?”
Ask:
“Am I connecting an agent to a capability, or an agent to another autonomous system?”
Once you answer that, the protocol choice usually gets much easier.
Key Takeaway: The real decision is not MCP vs A2A. It is capability integration vs agent collaboration.
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