# MCP Server Example

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

## 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.

![MCP Diagram](img/mcp-diagram-bg.png)

### 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
```
On Windows:

```cmd
pip install uv
```

Make sure to restart your terminal afterwards to ensure that the `uv` command gets picked up.

### Project Setup

1. clone and initialize the project:

```bash
# Create a new directory for our project
git clone https://github.com/wolderufael/docs-MCP-server.git
cd docs-mcp-server

# Create virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install dependencies
uv venv sync 
```

### 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 Cursor ai

1. Install Cursor ai from the official website
2. Configure Cursor to use your MCP server:

Edit `.cursor\mcp.json`:

```json
{
  "mcpServers": {
    "mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/YOUR/mcp-server",
        "run",
        "main.py"
      ]
    }
  }
}
```

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.