MCP Server Example
An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.
alejandro-ao
README
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 theYouTube 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
- Create and initialize the project:
# Create a new directory for our project
uv init mcp-server
cd mcp-server
# Create virtual environment and activate it
uv venv
source .venv/bin/activate # On Windows use: .venv\Scripts\activate
# Install dependencies
uv add "mcp[cli]" httpx
- Create the server implementation file:
touch main.py
Running the Server
- Start the MCP server:
uv run main.py
- The server will start and be ready to accept connections
Connecting to Claude Desktop
- Install Claude Desktop from the official website
- Configure Claude Desktop to use your MCP server:
Edit ~/Library/Application Support/Claude/claude_desktop_config.json
:
{
"mcpServers": {
"mcp-server": {
"command": "uv", # It's better to use the absolute path to the uv command
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/YOUR/mcp-server",
"run",
"main.py"
]
}
}
}
- Restart Claude Desktop
Troubleshooting
If your server isn't being picked up by Claude Desktop:
- Check the configuration file path and permissions
- Verify the absolute path in the configuration is correct
- Ensure uv is properly installed and accessible
- Check Claude Desktop logs for any error messages
License
This project is licensed under the MIT License. See the LICENSE file for details.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Mult Fetch MCP Server
A versatile MCP-compliant web content fetching tool that supports multiple modes (browser/node), formats (HTML/JSON/Markdown/Text), and intelligent proxy detection, with bilingual interface (English/Chinese).
AIO-MCP Server
🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows. Folk from
Persistent Knowledge Graph
An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.
Hyperbrowser MCP Server
Welcome to Hyperbrowser, the Internet for AI. Hyperbrowser is the next-generation platform empowering AI agents and enabling effortless, scalable browser automation. Built specifically for AI developers, it eliminates the headaches of local infrastructure and performance bottlenecks, allowing you to