MCP Server Example
About
A simple MCP server to search for documentation (tutorial)
Explore
- Demonstrates MCP Resources, Tools, and Prompts
- Example implementation in Python 3.10+
- Uses the official MCP SDK (1.2.0+)
- Connects to Claude Desktop and other MCP hosts
- Serves as a tutorial companion for a video guide
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Server ExampleCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
On MacOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Make sure to restart your terminal afterwards to ensure that the uv command gets picked up.
1. Create and initialize the project:
uv venv
source .venv/bin/activate # On Windows use: .venv\Scripts\activate
uv add "mcp[cli]" httpx
2. Create the server implementation file:
touch main.py
1. Start the MCP server:
uv run main.py
2. The server will start and be ready to accept connections
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server example": {
"mcp-server-example": {
"command": "uv",
"args": [
"init",
"mcp-server"
]
}
}
}
}McpServers
{
"mcp-server-example": {
"command": "uv",
"args": [
"init",
"mcp-server"
]
}
}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.
What is MCP?
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.
Key Benefits
- 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
Architecture Overview
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
Core MCP Concepts
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
System Requirements
- Python 3.10 or higher
- MCP SDK 1.2.0 or higher
- uv package manager
Getting Started
Installing uv Package Manager
On MacOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Make sure to restart your terminal afterwards to ensure that the uv command gets picked up.
Project Setup
1. Create and initialize the project:
```bash
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