MCP Server Example
# 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](https://youtu.be/Ek8JHgZtmcI).
## 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:
```bash
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
# 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
```
2. Create the server implementation file:
```bash
touch main.py
```
### Running the Server
1. Start the MCP server:
```bash
uv run main.py
```
2. The server will start and be ready to accept connections
## Connecting to Claude Desktop
1. Install Claude Desktop from the official website
2. Configure Claude Desktop to use your MCP server:
Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:
```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"
]
}
}
}
```
3. Restart Claude Desktop
## Troubleshooting
If your server isn't being picked up by Claude Desktop:
1. Check the configuration file path and permissions
2. Verify the absolute path in the configuration is correct
3. Ensure uv is properly installed and accessible
4. Check Claude Desktop logs for any error messages
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose of searching documentation for specific libraries.
The single tool name 'get_docs' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations to evaluate.
A single tool is too few for most server purposes, as it severely limits functionality and scope. This feels thin and incomplete for a documentation search server, which might benefit from additional tools like browsing documentation structure or getting library lists.
The tool surface is severely incomplete for a documentation search domain. It only supports searching text, with no tools for browsing, listing available libraries, or accessing documentation metadata, creating significant gaps that will hinder agent workflows.