I've lost count of how many "MCP vs A2A" explainers I've read that spend 2,000 words dancing around the answer. So here it is up front: MCP is how an AI agent talks to tools. A2A is how AI agents talk to each other. They solve different problems, they work at different layers, and in most real systems you'll end up using both.
Now let me actually show you what that means.
What MCP does (and doesn't do)
The Model Context Protocol, released by Anthropic in late 2024, is an open standard for connecting AI applications to external systems. The official docs describe it as "USB-C for AI applications," and that analogy holds up well. Just as USB-C gives you one port that works with displays, storage, power, and peripherals, MCP gives an AI agent one protocol that works with databases, APIs, file systems, and third-party services.
The architecture is straightforward client-server. An MCP host (your AI application, like Claude Desktop or VS Code with Copilot) creates MCP client instances that each maintain a connection to an MCP server. Each server exposes three types of context:
- Tools: functions the agent can call (search a database, create a file, send an email)
- Resources: data the agent can read (documents, database records, API responses)
- Prompts: reusable templates that shape how the agent processes information
MCP servers can run locally over stdio (great for development, single-user) or remotely over Streamable HTTP (production, multi-user). The protocol uses JSON-RPC 2.0 for message framing.
The WordPress.com announcement on March 20, 2026 is a clean example of MCP in action. WordPress built an MCP server that exposes your site's content, analytics, and settings as tools and resources. Connect Claude or ChatGPT as an MCP client, and suddenly your AI assistant can draft posts, update pages, and check traffic stats through natural conversation. Customers toggle capabilities on at wordpress.com/mcp and connect their preferred client. That's it. No custom API integration, no webhook plumbing.
What MCP does not do: it doesn't let one AI agent delegate work to another AI agent. Your Claude instance can use MCP to query a database, but it can't use MCP to ask a separate inventory-management agent to handle restocking. That's where A2A comes in.
What A2A does (and doesn't do)
The Agent-to-Agent protocol launched by Google in April 2025, now housed under the Linux Foundation as an open-source project. Where MCP standardizes the connection between an agent and its tools, A2A standardizes the connection between agents themselves.
The core mental model: A2A treats every agent as an opaque service with an HTTP endpoint. Agents don't share their internal memory, reasoning chains, or tool configurations with each other. Instead, they exchange structured messages through a task-based workflow.
The building blocks:
- Agent card: a JSON document published at a known URL that describes what an agent can do, what data types it supports, and how to authenticate. Think of it as a machine-readable business card.
- Task: the unit of work. A client agent creates a task, a remote agent processes it, and the task moves through states (submitted, working, completed, failed).
- Messages: the conversational back-and-forth within a task, each containing one or more parts (text, files, structured data).
- Artifacts: the outputs a remote agent produces as it works on a task.
A2A was designed around five principles that Google documented from day one: support natural agentic capabilities, build on existing standards (HTTP, JSON-RPC), implement enterprise-grade security by default, support long-running tasks, and stay modality-agnostic (text, audio, video, structured data all work).
Here's what makes A2A different from just making REST API calls between services: A2A agents are autonomous. A remote agent can ask clarifying questions, provide incremental updates, negotiate what it can deliver, and operate asynchronously over hours or days. Regular APIs assume request-response. A2A assumes collaboration.
What A2A does not do: it doesn't standardize how an agent connects to its own tools or data sources. An A2A agent might use MCP internally to access databases, or it might use raw API calls, or something else entirely. A2A doesn't care. It only governs the conversation between agents.
The architecture, side by side
Let me put this in concrete terms with one scenario.
A retail company wants an AI system that monitors inventory, detects low-stock items, and automatically reorders from suppliers.
With MCP alone, you'd build one agent that connects to your inventory database (MCP server), your order management system (MCP server), and your supplier's API (MCP server). The agent does everything. It checks stock levels, decides what to reorder, and places the order. This works fine for simple setups, but the agent needs access to everything, and it's doing all the reasoning in one context.
With A2A added, you split the work. An internal inventory agent uses MCP to monitor your database. When stock drops below threshold, it creates an A2A task delegating the purchase to an order agent. The order agent uses A2A to communicate with your supplier's agent (which you don't control and can't see the internals of). Each agent is specialized, each maintains its own tools and data, and the supplier's proprietary logic stays private.
| Dimension | MCP | A2A |
|---|---|---|
| Connection type | Agent to tool/data | Agent to agent |
| Transport | stdio or Streamable HTTP | HTTP |
| Message format | JSON-RPC 2.0 | JSON-RPC over HTTP |
| Discovery | Configured by the host | Agent cards at known URLs |
| State model | Stateless tool calls | Stateful task lifecycle |
| Visibility | Agent sees tool internals | Agents are opaque to each other |
| Launched by | Anthropic (late 2024) | Google (April 2025) |
| Governance | Open source | Linux Foundation |
Decision framework: when to use which
I've been building with both protocols for the past year. Here's the framework I actually use.
Use MCP when your agent needs to interact with specific data sources or services. If you're connecting an AI assistant to your company's databases, file systems, SaaS APIs, or internal tools, MCP is the answer. It's particularly good when you want multiple AI clients (Claude, ChatGPT, VS Code Copilot, Cursor) to all access the same backend capabilities through one standardized server.
Use A2A when you have multiple autonomous agents that need to collaborate. This is the right choice when agents are built by different teams (or different companies), when you want agents to remain opaque to each other, or when tasks are long-running and require back-and-forth negotiation. Enterprise multi-agent orchestration across organizational boundaries is where A2A earns its keep.
Use both when (this is most production systems) your agents each use MCP to access their own tools, and A2A to coordinate with each other. The A2A project's own documentation says it plainly: "Build with ADK (or any framework), equip with MCP (or any tool), and communicate with A2A."
Skip A2A when you have a single agent or a tightly coupled multi-agent system where all agents share the same codebase and trust each other completely. Frameworks like CrewAI or LangGraph handle internal orchestration without the overhead of a full inter-agent protocol.
Skip MCP when your agent's tool access is trivial (one or two API calls) and you don't need the standardization benefits. Direct API calls are fine when you're not building for reuse.
Getting started without the yak shaving
For MCP, the fastest path is picking an existing MCP server from the registry and connecting it to Claude Desktop or VS Code. You'll have a working integration in under ten minutes. Building your own server using the TypeScript or Python SDK takes an afternoon if you follow the official quickstart (which, refreshingly, includes all the steps).
For A2A, Google's codelabs walk through building a purchasing concierge with a client agent and two remote seller agents. It runs on Cloud Run, and the codelab covers agent cards, task creation, and the full message lifecycle. Budget a few hours.
For using both together, the pattern is: build MCP servers for each agent's tool access, then wrap each agent in an A2A-compatible HTTP endpoint with an agent card. The A2A Python and TypeScript SDKs handle the protocol plumbing.
What's actually changing right now
The WordPress.com MCP expansion matters because it represents a shift from "developers connecting tools" to "platforms shipping MCP servers as a first-class feature." When the CMS that powers 40%+ of the web adds MCP support, that's the protocol crossing from developer tooling into mainstream infrastructure.
On the A2A side, Google is adding A2A support to Agent Engine on Google Cloud, which means managed, production-grade agent hosting with A2A baked in. The Linux Foundation governance gives enterprises the confidence to build on it without worrying about a single vendor controlling the spec.
IBM's Agent Communication Protocol (ACP) was recently incorporated into the A2A project, consolidating two competing agent-communication standards into one. That's the kind of convergence that signals a protocol is winning.
The real question for 2026 isn't "MCP or A2A?" It's "how quickly can I get both running?" The protocols are complementary by design, the tooling is maturing fast, and the ecosystem is consolidating rather than fragmenting. That's rare in protocol wars, and it's worth building on.
Sage Thornton covers developer tools and guides for The Daily Vibe.
