A2A vs MCP: The Protocol Stack Every SaaS Leader Needs to Understand

Most SaaS founders are having the wrong conversation about AI. They are debating which LLM to use, how to fine-tune it, or whether to build a chatbot. Meanwhile, a far more consequential decision is crystallizing in the background: the protocols that govern how AI agents talk to tools, to data, and to each other.

Two open specifications are competing to define that layer. Model Context Protocol (MCP), backed by Anthropic and now adopted across the industry, and Agent to Agent (A2A), introduced by Google. If you are building a SaaS product in 2025 and beyond, understanding the difference is not optional. It will shape your architecture, your integrations, and your competitive positioning for years.

Why Protocols Matter More Than Models Right Now

Spend five minutes in any serious AI engineering conversation, and you will notice something: the model selection debate is largely over for most production use cases. GPT-4o, Claude 3.7, and Gemini 1.5 Pro are all capable enough. The differentiation has moved up the stack.

The real frontier is orchestration: how do you reliably connect an LLM to your CRM, your database, your internal APIs, and your third-party services? How do you let one AI agent hand off a task to a specialized subagent without that handoff becoming a brittle, one-off integration?

That is exactly the problem MCP and A2A are solving, and they are solving different layers of it.

MCP: The USB-C Port for AI Tools

Model Context Protocol, originally published by Anthropic in late 2024, is best understood as a standardized interface between an AI model and the external world. Think of it as the USB-C port of the agentic stack. Before USB-C, every device had its own charging standard. Before MCP, every AI tool integration was a custom one-off.

MCP defines a client-server architecture. The AI model or the orchestration layer around it acts as the MCP client. Your tool, API, or data source exposes an MCP server. The server publishes a manifest of its capabilities, and the model calls them in a structured way.

What MCP Looks Like in Practice?

Say you are building a SaaS platform for operations teams. Your product connects to Salesforce, Jira, and Slack. Without MCP, you write three separate integration layers, each with custom parsing, error handling, and prompt engineering to coax the model into using them correctly.

With MCP, each integration exposes a server. The model discovers available tools from these servers, understands their schemas, and calls them in a standard way. You write the integration once, in a portable format that any MCP-compatible model or agent runtime can consume.

This portability is the point. MCP has already seen broad adoption across Cursor, Sourcegraph, Zed, and a growing ecosystem of agent frameworks. When Anthropic open-sourced the spec and other vendors started supporting it, MCP stopped being an Anthropic feature and became infrastructure.

Figure 1: MCP Architecture — A standardized interface layer connecting LLM agents to any external tool, API, or data source via MCP Servers

A2A: The Protocol for Agent Collaboration

Google’s Agent-to-Agent (A2A) protocol addresses a different problem entirely. Once you accept that sophisticated AI workflows will involve multiple specialized agents working in concert, you need a standard for how those agents communicate with each other.

A2A defines how one agent, the client agent, can discover, call, and coordinate with another agent, the remote agent. The spec introduces the concept of an Agent Card: a structured JSON document, published at a well-known URL, that describes what an agent can do, what authentication it requires, and how to interact with it.

The Agent Card Concept

Think of an Agent Card as the OpenAPI spec for an AI agent. Just as developers use Swagger docs to understand a REST API, a client agent reads an Agent Card to understand a remote agent’s capabilities before routing work to it.

This matters because it enables dynamic discovery. Rather than hardcoding which agents talk to which, you can build orchestration layers that query capabilities at runtime, route tasks to the most appropriate specialized agent, and degrade gracefully when an agent is unavailable.

Where A2A Changes the Architecture

Consider a complex enterprise workflow: a user asks an AI assistant to produce a competitive analysis, update the CRM based on recent call notes, draft a follow-up email, and schedule a team review meeting.

With A2A, a master orchestrator agent routes each subtask to a specialized agent: a research agent, a CRM agent, an email drafting agent, a calendar agent. Each specialized agent may itself use MCP to connect to its underlying tools. The two protocols are complementary: MCP handles tool access within an agent, and A2A handles task delegation across agents.

Figure 2: A2A Protocol Stack — An orchestrator agent delegates tasks to specialist agents via A2A, while each specialist uses MCP to reach its underlying tools

Side by Side: MCP vs A2A

What This Means for Your SaaS Architecture

If you are a technical founder or CTO, here is how to translate this into decisions you will face in the next twelve months.

Build MCP Servers for Your Core Product Surfaces

If your product sits on data or capabilities that agents should be able to access, publish an MCP server. This is not speculative: customers are already building agentic workflows that will want to connect to your product. Being MCP-compatible is becoming a table-stakes integration requirement, the same way having a REST API was in 2015.

Prioritize exposing your highest-value data surfaces: read access to core entities, write access to key actions, search over domain-specific content. Keep the server stateless, document the tool schemas carefully, and version them with the same discipline you would any public API.

Design Your Internal Architecture for Agent Delegation

Even if you are not publishing A2A agents today, design your internal AI features to be decomposable. The monolithic AI assistant that does everything is already showing its limits in production. Reliability, debuggability, and latency all improve when you break complex workflows into focused subagents.

Watch the Ecosystem Closely

MCP adoption is already ahead of most founders’ awareness. Dozens of popular developer tools have shipped MCP servers in the past six months. The velocity of the ecosystem suggests that, within a year, not having MCP coverage for your product will feel like not having a Zapier integration in 2020.

A2A is earlier. Google has open-sourced the spec, and several frameworks are building support, but the enterprise patterns for multi-agent orchestration are still being worked out. This is the right time to prototype, not to build production dependencies.

The Strategic Risk Most Teams Are Missing

Here is the underappreciated risk: if you do not proactively publish MCP access to your product, someone else will define how AI agents interact with it. Scrapers, unofficial integrations, and third-party MCP servers will fill the gap, and they will not represent your product’s capabilities faithfully or safely.

The companies that will win in the agentic era are those that make their product a first-class citizen in agent workflows. That means owning your MCP server, thoughtfully designing your Agent Card, and building a developer experience around agentic access alongside your human-facing UI.

This is not a distant concern. Enterprises evaluating SaaS products are already asking whether tools are MCP-compatible, whether they can be orchestrated programmatically by AI workflows, and whether the vendor has thought carefully about agentic access control.

Where to Start

The protocol layer of the agentic stack is not an engineering concern that you can safely delegate down. It is a product and strategy decision that will shape your market positioning, your enterprise sales conversations, and your architecture for the next three to five years.

Start with MCP. Audit your product surfaces and identify the two or three highest-value capabilities you would want an AI agent to access. Build a server, test it against Claude and other agent runtimes, and ship it. Treat it like a public API: document it, version it, monitor it.

Follow A2A closely. The multi-agent coordination patterns it enables are where enterprise AI workflows are heading. You do not need to build on A2A today, but you should understand the model and start thinking about how your product fits into an ecosystem of collaborating agents rather than as a standalone tool.

The protocol stack is being set right now. The founders who understand it will make better product decisions, move faster in enterprise sales, and build architectures that age well. The ones who wait will spend the next two years retrofitting.