A2A vs. MCP: Choosing the Right Interop Standard for Your Agents

“Should we be using A2A for this?” is the question that shows up in every design review once a system grows past about three agents. It’s a fair question that most teams answer badly, because the two dominant agent protocols get lumped together when they solve different problems at different layers.

The short version: MCP connects an agent to its tools. A2A connects one agent to another. They are complementary, not competing — and choosing wrong is expensive in both directions.

The one-sentence distinction

From the Model Context Protocol’s own definition:

MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems. Using MCP, AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts).

And from the A2A protocol documentation:

The Agent2Agent (A2A) Protocol is an open standard for seamless communication and collaboration between AI agents. In a world where agents are built using diverse frameworks and by different vendors, A2UI provides the definitive common language for agent interoperability.

The A2A docs are unusually direct about the relationship: MCP and A2A “are not competitors — they are highly complementary. They solve two different problems and are designed to work together.”

Vertical vs. horizontal

The clearest mental model comes from the A2A protocol’s comparison page:

• MCP is vertical. It deepens a single agent. Every MCP connection hands that agent another tool, resource, or skill. The more you connect, the more that agent can do on its own.
• A2A is horizontal. It connects agents across an ownership boundary — another team, another department, a partner organization. Your agent reaches out to discover, negotiate, and exchange information.

Or as the same page puts it: “Used together, MCP gives each agent depth, and A2A gives your system reach.”

Side-by-side

Dimension	MCP	A2A
Primary purpose	Agent–tool integration	Agent collaboration
Interacts with	Tools, resources, prompts	Autonomous agents
Topology	Client–server, one agent owns the connection	Peer-to-peer across orgs
Payload shape	Single structured call → structured result	Multi-turn dialogue, tasks, artifacts
Statefulness	Typically stateless per call	Stateful, long-running tasks
Discovery	Server capabilities at connect time	Agent Cards describing skills
Transport	JSON-RPC over stdio/HTTP	JSON-RPC over HTTP + SSE + push
Governance	Anthropic-originated, open	Google-donated, Linux Foundation
Failure unit	A failed tool call	A failed delegated task

That last row drives most architecture decisions. When a tool call fails, you retry it. When a delegated task fails, you need a status model, partial results, and possibly a human.

How to choose

Reach for MCP when:

• Your agent needs a database, API, filesystem, or SaaS action
• You are exposing reusable tools to multiple AI clients
• The caller should orchestrate the workflow and consume individual capabilities
• The boundary is within an application you own

Reach for A2A when:

• You’re delegating a goal to another autonomous or semi-autonomous agent
• The remote agent should retain control of its own tools, memory, and execution plan
• You need capability discovery, task status, messages, and returned artifacts
• Agents on different frameworks must interoperate without custom glue

The Redis engineering blog frames it well: “reach for A2A when work delegates across a boundary you don’t control and neither side can expose internal tools or memory, when tasks run long enough to need a stateful lifecycle with approval gates, or when agents on different frameworks must interoperate without custom glue.”

And the corollary that saves teams from over-engineering: A2A gets read as “the multi-agent protocol,” so any system with more than one agent starts to feel like it needs one. It usually doesn’t. Two agents inside one service, sharing a database, do not need a wire protocol between them.

The canonical composition

The A2A documentation’s example is the mechanic shop:

• A customer talks to the shop’s agent (A2A or plain UI)
• The shop’s agent talks to a parts supplier agent it doesn’t own (A2A)
• The mechanic agent uses its own diagnostic tools internally (MCP)

Or in the layered form:

User
 └─ Client Agent ────────────── A2A ───────────▶ Remote Agent
                                     │
                                     ├─ MCP ─▶ inventory DB
                                     ├─ MCP ─▶ parts catalog API
                                     └─ MCP ─▶ order service

The A2A docs state the rule plainly: “Build with ADK (or any framework), equip with MCP (or any tool), and communicate with A2A, to remote agents, local agents, and humans.”

Note also what A2A explicitly is not — from the same docs:

• Not an agent development kit (LangGraph, CrewAI, ADK build those)
• Not a sub-agent or tool-call protocol — “use your framework’s native primitives, or MCP, for those”
• Not a replacement for MCP
• Not a messaging app — it’s machine-to-machine

That third bullet is the one people skip.

A decision flowchart

Is the thing you're calling autonomous — does it plan, keep its own
state, and make its own tool decisions?
├── No  → It's a tool. Use MCP (or just an SDK/HTTP call).
└── Yes → Does it belong to a different team/vendor/org, or must it
          interoperate across frameworks?
          ├── No  → Use your framework's native sub-agent primitive.
          └── Yes → Use A2A.

And if you have MCP tools and A2A peers, the layering rule: A2A at the coordination layer, MCP at the execution layer.

Integration details worth knowing before you commit

MCP lifecycle. MCP defines a three-phase client–server lifecycle — initialize, use, shutdown — with capability negotiation at connect time. Plan for connection management, and for servers that need authentication (OAuth, tokens) in production.

A2A discovery. A2A agents publish an Agent Card — a machine-readable description of what they can do and how to invoke them. Your client agent fetches cards to decide who to delegate to. Someone has to own card publication and versioning.

A2A’s async shape. A2A runs JSON-RPC 2.0 over HTTP and adds Server-Sent Events streaming plus push notifications, specifically “for tasks that can run for hours or days.” If your task model doesn’t have durable state transitions (submitted → working → input-required → completed), you’re not ready for A2A regardless of protocol support.

Governance differences. MCP was launched by Anthropic in late 2024; A2A was introduced by Google in April 2025 and donated to the Linux Foundation in June 2025 with founding members including AWS, Microsoft, Salesforce, and SAP. If procurement or standards alignment matters to you, these lineage differences matter.

Security models differ. MCP security is about authenticating a client to a server and scoping tool access — least privilege on tools is the main lever (the OWASP LLM Top 10’s LLM07, insecure plugin design, applies directly). A2A security is about authenticating organizations to each other and authorizing delegated tasks — a trust-boundary problem, not a scoping problem.

Anti-patterns

A2A for internal sub-agents. Adds a network hop, a serialization boundary, and a discovery step to what should be a function call.

MCP for cross-org delegation. You’d be handing a partner direct access to your agent’s internals instead of negotiating a task boundary. MCP assumes the client owns the orchestration; across an ownership boundary it doesn’t.

One mega-MCP-server-of-everything. Fine-grained, domain-scoped servers compose; a monolith re-creates the coupling you adopted protocols to avoid.

Skipping the Agent Card contract. If capabilities are discovered dynamically but your client hard-codes assumptions about them, discovery is theater.

Protocol-first design. Start from the ownership boundary and the task lifecycle; pick the protocol second. Teams that start from “we need A2A” usually discover they needed MCP, or nothing.

Migration advice

If you’re mid-transition:

1. Wrap existing tool access in MCP inside each agent — this is low-risk and pays off regardless of what you decide about A2A.
2. Identify the actual cross-boundary delegations you perform today, most of them probably over bespoke HTTP or Slack messages.
3. Convert one of those to A2A as a pilot — pick one with a clean task lifecycle and a partner who wants the integration anyway.
4. Keep framework-native primitives for everything internal.

Wrapping up

MCP and A2A standardize two different axes: what an agent can do, and who an agent can work with. MCP is vertical depth, A2A is horizontal reach. Almost every real system ends up needing both — MCP for execution, A2A for coordination — but almost no system needs A2A on day one. Draw the ownership boundary first; the protocol choice usually follows without much debate.

References

• Model Context Protocol documentation
• A2A and MCP — A2A protocol documentation
• A2A protocol homepage
• A survey of agent interoperability protocols (arXiv)