How to Build an MCP Server: A Step-by-Step Guide

Prerequisites

Before building your MCP server, you need:

• Python or TypeScript installed on your development machine
• An MCP SDK package for your chosen language

Step 1: Choose Your Language and Install the MCP SDK

Select either Python or TypeScript based on your team’s expertise. Both have mature SDK ecosystems.

For Python, install the SDK:

pip install mcp

For TypeScript:

npm install @modelcontextprotocol/sdk

Consider picking up Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming if you’re new to Python. TypeScript developers benefit from Learning TypeScript: Enhance Your Web Development Skills Using Type-Safe JavaScript.

Step 2: Define Your Server’s Capabilities

MCP servers expose three capability types:

• Tools – Functions the AI can call to perform actions
• Resources – Files or data the AI can read
• Prompts – Reusable templates for common interactions

Step 3: Implement Your First Tool

Here’s a Python example using FastMCP:

from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My Server")
@mcp.tool()
def add(a: int, b: int) -> int:
    return a + b
mcp.run()

TypeScript equivalent:

import { Server } from "@modelcontextprotocol/sdk";
const server = new Server({ name: "my-server" });
server.tool("add", { a: "number", b: "number" }, ({ a, b }) => a + b);

Step 4: Run the Server Over a Supported Transport

Choose your transport based on deployment:

• stdio – For desktop clients running locally
• HTTP/SSE – For remote servers accessible over the network

Step 5: Connect to an MCP-Compatible Client

Test your server with an MCP client:

• ChatGPT (where MCP support is available)
• Claude Desktop
• Other MCP-compatible clients

Common Tool Types You Can Expose

MCP servers commonly expose:

• Database access for querying internal data
• Internal API endpoints
• Filesystem operations
• Cloud services (GitHub, Slack, Google Drive)
• Custom automation workflows

For architecture guidance, see System Design Interview – An Insider’s Guide: Volume 2.

Best Practices for Production-Ready MCP Servers

Follow these guidelines for reliable MCP servers:

• Validate every input before processing
• Return structured JSON responses
• Use authentication for remote servers
• Restrict dangerous operations
• Write clear tool descriptions

Advanced developers should reference Fluent Python: Clear, Concise, and Effective Programming or Programming TypeScript: Making Your JavaScript Applications Scale.

Conclusion

You now have a working MCP server. Continue learning with the Model Context Protocol documentation, review the MCP specification, and explore the Official MCP GitHub organization for examples and community tools.