MCP vs Traditional API Architecture

...explained visually!

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MCP vs Traditional API Architecture

Traditional APIs were built for apps to talk to servers.

You have a client (web or mobile app), which sends HTTP requests through an API gateway, and the gateway routes to different services.

This works great for applications. But AI agents aren’t apps.

Here’s the problem:

When you want an AI agent to use a tool, like querying a database, accessing files, or calling an API, you have to write custom integration code for each one. Every tool is different and every integration is bespoke.

MCP solves this and the visual below differentiates the architectural difference.

Instead of building custom integrations, MCP provides a universal protocol that sits between AI clients (Claude, IDEs, agents) and tools/APIs.

• One protocol to connect to any tool

• The AI doesn’t care what’s behind the server, like a database, file system, web API

• Tool providers build one MCP server, and it works with any AI client.

The visual above shows this clearly: instead of an API gateway routing traffic to individual services, MCP creates a universal layer between AI agents and backend resources.

If you don’t know MCPs, read the guidebook linked above.

And if you want to dive into core MCP engineering, we covered all these details (with implementations) in the MCP course:

• ​Part 1 covered MCP fundamentals, the architecture, context management, etc. →​​​​

• ​Part 2 covered core capabilities, JSON-RPC communication, etc. →​​​​

• ​Part 3 built a fully custom and local MCP client →​​​​

• ​Part 4 built a full-fledged MCP workflow using tools, resources, and prompts →​​​​

• ​​​​Part 5 taught how to integrate Sampling into MCP workflows →​​​​

• ​Part 6 covered testing, security, and sandboxing in MCP Workflows →​​​​

• ​Part 7 covered testing, security, and sandboxing in MCP Workflows →​​​​

• ​Part 8 integrated MCPs with the most widely used agentic frameworks: LangGraph, LlamaIndex, CrewAI, and PydanticAI →​​​​

• ​​​​P​​​​​​art 9 covered using LangGraph MCP workflows to build a comprehensive real-world use case →​

👉 Over to you: What is your perspective on MCP vs Traditional API?

Thanks for reading!