MCP vs

MCP and A2A: A Network Engineer's Mental Model for Agentic AI

The Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol have been getting a lot of attention. MCP made a big splash when Anthropic published it in late 2024, and companies saw its value for abstracting APIs into natural language for LLMs.

Then, in April 2025, Google introduced A2A to help AI agents discover each other's capabilities. While MCP saw fast adoption, A2A's growth has been slower. This has led some to believe it's a format war, like Blu-ray vs. HD-DVD, with MCP as the winner. That's not quite right.

What MCP Actually Does

MCP is a way for an LLM to understand and use external tools. Before MCP, exposing tools through raw APIs was clumsy and difficult to scale. LLMs operate with natural language, so they need a way to interpret a task and find the right tool for the job without getting bogged down by API specifics.

MCP solves this. It also helps with problems like API versioning. If an API changes, how does an LLM know? MCP was built to handle exactly these kinds of issues.

The architecture works well up to a point. As you add more tools to an MCP server, the manifest sent to the LLM can become huge, consuming the entire prompt context window. This is a fundamental constraint, even for models with massive context windows. Managing this prompt budget is a core part of effective agent design, a skill covered in prompt engineering.

This Is Where A2A Comes In

A2A operates at a higher level. It doesn't deal with individual tools or API details. Instead, it uses "Agent Cards," which are high-level descriptions of what an agent can do, not a list of its tools.

A2A is designed exclusively for communication between agents. It can't interact directly with tools or end systems like MCP can. It's a protocol for agent discovery and delegation.

So, Which One Do You Use?

The answer is both. If you're building a simple system with one supervisor agent and a set of tools, MCP alone might be all you need, provided the prompt stays within the LLM's context limits.

But for a multi-agent system, you'll likely need A2A. Think of a supervisor agent handling a request to "analyze Wi-Fi roaming problems." Instead of having every tool exposed to it, the supervisor uses A2A to find specialized agents for RF analysis or user authentication based on their high-level Agent Cards.

Once the right agent is found, that agent can then use MCP to discover and use its specific tools. In this flow, A2A provides scalable routing between agents, while MCP provides precise execution at the tool level. This isn't an "MCP vs. A2A" decision; it's an architectural one.

A Networking Analogy

A good mental model comes from computer networking. Early networks were small, self-contained Layer-2 domains. As they grew, the limits of Layer-2 were hit, requiring Layer-3 routers and routing protocols. Routers create boundaries and summarize network information, preventing broadcast storms and enabling scale.

This maps directly to agentic protocols:

• MCP is like Layer 2: It provides detailed, direct access within a local domain but doesn't scale indefinitely.
• A2A is like Layer 3: It acts as the routing boundary, aggregating high-level information and providing a gateway to the broader agentic network.

Just as modern networks are built on both Layer 2 and Layer 3, sophisticated AI systems will need the full stack. Both MCP and A2A are aligned with the Linux Foundation and should be seen as critical, complementary layers.

The teams that recognize this early will be the ones that successfully build their agentic systems into durable, production-grade architectures.