From Monolith to MCP: How Model Context Protocol Transformed Our Architecture

We started as a REST API with 10 endpoints. Now we have 70 MCP tools across 3 services with triple transport. MCP gave us what REST could not: a standard way for AI to discover and use tools on its own. AI becomes the client, not you.

From Monolith to MCP: How Model Context Protocol Transformed Our Architecture

REST API works great when the client is a human. When the client is AI, you need a different protocol.

Why REST Is Not Enough for AI

REST API works like this: a developer reads documentation, writes integration code, hardcodes endpoints. Works perfectly for web apps.

But when your "client" is an LLM that must decide on its own which tool to call:

• REST has no standard tool discovery
• No built-in parameter descriptions for AI
• Every integration is custom code
• Batch, streaming, cost estimation — all separate

What MCP Provides

Model Context Protocol is a standard by Anthropic for AI interaction with external tools.

Tool Discovery

GET /api/tools → full catalog with JSON Schema for every parameter

The AI receives a list of all 70 tools with descriptions, parameter types, constraints — and decides on its own what to call.

Standardized Schema

Every tool is described the same way:

• name — unique identifier
• description — what it does (with cost hints)
• inputSchema — JSON Schema for parameters
• outputSchema — result format

Three Transports

stdio for local clients, HTTP for web, SSE for streaming — the same set of tools via any protocol.

Our Migration

Before: REST Monolith

• 10 endpoints with hardcoded logic
• Every frontend component knows a specific URL
• Adding a tool = adding a route + controller + documentation

After: MCP Architecture

• 70 tools via BaseToolHandler
• AI selects tools by description on its own
• Adding a tool = adding a handler class + one-line registration

The Key Mindset Shift

REST: you design an API for a developer who will write code.

MCP: you design an API for AI that will decide on its own when and what to call.

This changes everything — from naming to descriptions, from parameter structure to error format. AI needs clear descriptions, cost hints, examples — things that in REST live in documentation, but in MCP live right in the schema.

MCP is not a silver bullet. But for AI-first products, it is the best standard that exists today.