Most "MCP vs API" posts treat the two as competing standards. They're not. MCP, traditional REST APIs, and Google's A2A protocol solve three different problems, and confusing them is why so many agent architectures end up over-engineered or under-powered.
This post is the cheat sheet developers actually need: what each protocol is for, when to pick which one, and how they fit together in a production agent stack.
Key Takeaways
- REST APIs are for clients calling services - humans, browsers, and code making predictable function calls
- MCP is for AI agents calling tools - it standardizes how an agent discovers and invokes capabilities across any tool provider
- A2A is for agents calling other agents - it adds identity, capability negotiation, and turn-based conversation between agentic systems
- They are complementary, not competing - a production AI system in 2026 typically uses all three at different layers
- The decision is not "which one wins" but "which layer am I designing"
Short Answer
How are MCP, REST APIs, and A2A different? REST APIs let traditional clients call services in a predictable, request-response shape. MCP lets AI agents discover and call external tools through a standard interface, regardless of which vendor built the tool. A2A lets multiple AI agents communicate, negotiate capabilities, and pass tasks between each other. You almost certainly need REST APIs in your stack today; you probably need MCP for agent integration; you only need A2A when multiple independent agents need to coordinate.
Primary sources: Anthropic's MCP specification, Google's A2A protocol, and Anthropic's tool use documentation. Definitions in this post follow those primary sources, not secondary tutorials.
The Three Layers, in Plain English
Think of an agent system as three concentric layers:
| Layer | What it does | Protocol |
|---|---|---|
| Outer | A user (or client app) talks to a service | REST API |
| Middle | An agent inside the service uses tools | MCP |
| Inner | Multiple agents coordinate with each other | A2A |
Every layer has different requirements. REST APIs are predictable and stateless. MCP is dynamic and tool-aware. A2A is conversational and capability-aware. Trying to use one protocol for all three layers is the most common architecture mistake in 2026.
"MCP is not a replacement for REST. It's a layer on top of how agents talk to anything - including REST APIs underneath."
REST APIs: The Outer Layer
REST APIs are what you already know. A client (browser, mobile app, server) sends an HTTP request to a known endpoint with known parameters, and gets a known response shape.
GET /api/v1/skills/mcp-explained HTTP/1.1
Host: api.openbooklet.com
Authorization: Bearer ...
200 OK
{ "name": "mcp-explained", "version": "1.0.0", ... }
Strengths
- Predictable, well-tooled, decades of best practices
- Works in any language with an HTTP client
- Easy to cache, rate-limit, and monitor
When REST is the right answer
- Your client is a browser, mobile app, or traditional server
- The interaction is request-response, not multi-turn
- You need maximum compatibility with existing systems
REST APIs are not going away. Every agent system in production has REST APIs at the outer edges - for user requests, billing, auth, and data ingestion. The mistake is using REST as the only protocol when an agent in the middle needs richer interaction with tools.
MCP: The Middle Layer
MCP (Model Context Protocol), introduced by Anthropic in November 2024, is the protocol agents use to discover and call external tools. Instead of every AI vendor inventing their own format for tool calls, MCP gives all of them a common one.
{
"method": "tools/call",
"params": {
"name": "search_skills",
"arguments": { "query": "MCP server", "limit": 5 }
}
}
What MCP standardizes
- Discovery - how an agent learns what tools exist
- Invocation - how the agent calls a tool
- Resources - how an agent reads structured data (files, DB rows, etc.)
- Prompts - how a tool exposes templated workflows
When MCP is the right answer
- You're building an AI agent that uses tools - any tools
- You want to support multiple AI vendors (Claude, OpenAI, Cursor, etc.) without rewriting integrations
- You're publishing a tool for AI agents to consume
You can serve the same backend behind both REST and MCP. Many production systems do exactly this - REST for human/client traffic, MCP for agent traffic - using the same business logic underneath. This is the pattern OpenBooklet uses for its public API.
When MCP is overkill
- A single agent calling a single tool you already control end-to-end
- A non-agent client (no LLM in the loop)
- A purely internal microservice mesh between traditional services
A2A: The Inner Layer
A2A (Agent2Agent) is the newest of the three. Released by Google in 2024, A2A is what multiple agents use to talk to each other - not for tool calls, but for delegation, negotiation, and multi-turn coordination.
The protocol adds three things REST and MCP don't have:
- Agent identity and capability cards - each agent declares who it is and what it can do
- Task lifecycle - submit, accept, work, complete, with state shared across both agents
- Multi-turn negotiation - agents can ask clarifying questions before committing to a task
"A2A is what happens when you stop thinking of agents as functions and start thinking of them as colleagues."
When A2A is the right answer
- A research agent needs to delegate writing to a writing agent
- A planning agent needs to hand specific subtasks to specialist agents
- Multiple agents from different vendors need to coordinate on a long-running task
When A2A is overkill
- A single agent doing everything itself
- A simple "router" pattern where one agent picks a tool and calls it (MCP handles this)
- Internal multi-agent systems where you control all the agents and a shared message bus is simpler
A2A solves a real problem, but the problem is rare today. Most production agent systems in 2026 are still single-agent + many tools. Multi-agent coordination is a 2026–2027 wave, not a 2025 reality.
The 5-Step Decision Process
When you're designing an agent system, walk through these questions in order:
- Is the caller a human or a non-agent client? If yes → REST API. Stop here for that interaction.
- Is an LLM agent calling a tool or external capability? If yes → MCP for that call. Underneath, the tool can use REST internally - that's fine.
- Are multiple agents coordinating on a long-running task? If yes → A2A between them. If no, skip A2A entirely.
- Are you publishing a capability for other people's agents to consume? If yes → MCP server. This is where reach matters more than internal elegance.
- Is the integration purely internal with no external agent traffic ever? If yes, stick with REST or gRPC; don't adopt MCP just because it's trending.
The answer to "which protocol" is almost never "one of them." It's usually a layered combination.
A Real Production Stack
Here's what a typical 2026 agent product looks like, all three protocols included:
| Component | Protocol | Why |
|---|---|---|
| User → web app | REST + WebSocket | Standard browser-server |
| Web app → backend services | REST | Predictable, cacheable |
| Backend → LLM provider | Provider SDK | Vendor-specific (Anthropic, OpenAI, etc.) |
| LLM agent → tools | MCP | Vendor-neutral tool access |
| Research agent → writing agent | A2A | Specialist coordination |
| Tool servers → upstream services | REST | Existing third-party APIs |
Notice each protocol is doing exactly one job. Every layer has a different shape, and forcing them into one protocol is what breaks production systems.
The most common architecture mistake in 2026 is using MCP as a transport for non-agent traffic. MCP's design assumes an LLM is in the loop. Using it as a generic RPC for traditional services adds complexity without benefit.
What the Numbers Say
The three protocols are all growing, but at different rates:
- MCP had over 5,000 community-built servers by mid-2025 and is now adopted by Microsoft, Google, OpenAI, JetBrains, and Amazon as a first-class integration. (Anthropic's MCP announcement)
- REST APIs continue to dominate - over 80% of public APIs still use REST, and that's not changing soon. (Postman 2024 State of the API report)
- A2A is younger, with adoption mostly in research and multi-agent system experiments through 2025–2026. Real production multi-agent systems are still rare.
The honest takeaway: invest in MCP knowledge today, REST mastery is non-negotiable, A2A awareness is enough until you have a real multi-agent need.
FAQ
Can MCP and REST coexist in the same backend?
Yes, and they typically do. Many teams expose the same business logic over both - REST endpoints for traditional clients, MCP tools for AI agents. The shared layer is the actual logic; the protocol is just the wrapper.
Is MCP only for Anthropic / Claude?
No. MCP was created by Anthropic but is an open protocol. It's adopted by OpenAI, Google, Microsoft, Cursor, JetBrains, Amazon, and thousands of community servers. A tool you build with MCP works with any compliant client.
Should I learn A2A before I have a multi-agent need?
Not deeply. Skim the spec so you recognize the problem it solves. Implementing A2A before you have multiple agents coordinating is premature - the protocol's complexity only pays off when you actually have agents from different systems talking to each other.
What about gRPC, GraphQL, or SOAP?
Those are alternatives to REST at the outer layer. They don't compete with MCP or A2A. Pick whichever fits your client constraints; the agent layer above it doesn't care whether the underlying transport is REST, gRPC, or GraphQL.
How do I publish my tool so any agent can use it?
Build it as an MCP server. Once it speaks MCP, any compliant client (Claude, Cursor, OpenAI, etc.) can discover and call it without custom integration work. You can also list it on OpenBooklet to make it discoverable to the agent ecosystem.
Closing Key Takeaways
- Three layers, three protocols - REST for clients, MCP for tools, A2A for agent-to-agent
- They are complementary - not a horse race; production systems use all three
- The decision is layer-by-layer - walk the 5-step process before reaching for any protocol
Further reading: MCP Explained: The USB-C of AI Agents | MCP vs A2A vs ACP: The Only Comparison Guide You Actually Need | Browse the Learning hub | Explore MCP servers on OpenBooklet