mcp-server-example

This repository contains an implementation of a Model Context Protocol (MCP) server for educational purposes. This code demonstrates how to build a functional MCP server that can integrate with various LLM clients.

To follow the complete tutorial, please refer to the YouTube video tutorial.

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools.

• A growing list of pre-built integrations that your LLM can directly plug into
• Flexibility to switch between LLM providers and vendors
• Best practices for securing your data within your infrastructure

MCP follows a client-server architecture where a host application can connect to multiple servers:

• MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
• MCP Clients: Protocol clients that maintain 1:1 connections with servers
• MCP Servers: Lightweight programs that expose specific capabilities through the standardized Model Context Protocol
• Data Sources: Both local (files, databases) and remote services (APIs) that MCP servers can access

MCP servers can provide three main types of capabilities:

• Resources: File-like data that can be read by clients (like API responses or file contents)
• Tools: Functions that can be called by the LLM (with user approval)
• Prompts: Pre-written templates that help users accomplish specific tasks

• Python 3.10 or higher
• MCP SDK 1.2.0 or higher
• uv package manager

On MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Make sure to restart