트랜스포머 모델을 이용한 재무 예측

현대 트랜스포머 아키텍처가 시장 예측에서 기존 시계열 분석을 어떻게 능가하고 있는지 알아봅니다. This guide shows a minimal setup that still supports real endpoints and auth.

Introduction

The Model Context Protocol (MCP) is rapidly becoming the standard for connecting AI models to external tools and data. While the official documentation covers a lot of ground, many developers get stuck on the initial setup.

In this tutorial, we'll strip away the complexity and build a production-ready MCP server in just 15 minutes. We'll focus on the core components: the server instance, tool definitions, and the transport layer.

Step 1: Scaffolding the Project

First, let's create a new directory and initialize a TypeScript project. We'll use ts-node for development to keep things fast.

mkdir my-mcp-server
cd my-mcp-server
npm init -y
npm install @modelcontextprotocol/sdk zod
npm install -D typescript @types/node ts-node

Step 2: Defining Your Tools

The core of an MCP server is its tools. Tools are functions that the AI model can call. Let's define a simple weather tool using Zod for schema validation.

"Tools are the hands of the AI. Without them, models can only think, not act."

Step 3: The Server Instance

Now, let's wire everything up. We'll create an McpServer instance and register our tool.

Wrapping Up

You now have a fully functional MCP server running locally. From here, you can add more complex tools, implement authentication middleware, or deploy it to a serverless function.

The beauty of MCP is its composability. This server can now be used by any MCP-compliant client, including Claude Desktop, various IDEs, and your own custom agents.