Norbok7/Codelabs-MCP-Server

Absolutely, let's walk through setting up your custom GPT—Codelabs Learning Assistant 2025—as a Dockerized MCP (Model Context Protocol) server. This will allow your students to run it locally using Docker and interact with it through Visual Studio Code (VS Code).

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🧰 Step 1: Install Docker

For Windows:

1. Download Docker Desktop:

   • Visit the Docker Desktop for Windows page.
   • Click on "Download for Windows".(datacamp.com)

2. Install Docker Desktop:

   • Run the downloaded installer.
   • Follow the installation prompts.
   • During installation, ensure that the option to use WSL 2 is selected if prompted.(datacamp.com)

3. Start Docker Desktop:

   • After installation, launch Docker Desktop from the Start menu.
   • Wait for Docker to initialize; you'll see the Docker icon in your system tray when it's ready.(datacamp.com)

4. Verify Installation:

   • Open Command Prompt or PowerShell.

   • Type the following command and press Enter:

     docker --version
     

   • You should see the Docker version information displayed.(knowledgehut.com)

For macOS:

1. Download Docker Desktop:

   • Visit the Docker Desktop for Mac page.
   • Click on "Download for Mac".

2. Install Docker Desktop:

   • Open the downloaded .dmg file.
   • Drag the Docker icon to your Applications folder.(datacamp.com)

3. Start Docker Desktop:

   • Launch Docker from your Applications folder.
   • Wait for Docker to initialize; the Docker icon will appear in your menu bar when it's ready.

4. Verify Installation:

   • Open Terminal.

   • Type the following command and press Enter:

     docker --version
     

   • You should see the Docker version information displayed.(knowledgehut.com)

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🛠️ Step 2: Prepare Your MCP Server Code

1. Create a Project Directory:

   • Create a new folder on your computer, e.g., codelabs-mcp-server.

2. Create the Server Script:

   • Inside this folder, create a file named server.py.

   • Open server.py in your preferred code editor and paste the following code:

     from mcp.server.fastmcp import FastMCP
     import openai
     import os
     
     mcp = FastMCP("codelabs-assistant")
     
     @mcp.tool()
     async def answer_question(question: str) -> str:
         """Answers a question using the GPT model."""
         openai.api_key = os.getenv("OPENAI_API_KEY")
         response = openai.ChatCompletion.create(
             model="gpt-4",
             messages=[{"role": "user", "content": question}]
         )
         return response.choices[0].message["content"]
     
     if __name__ == "__main__":
         mcp.run(transport="stdio")
     

3. Create a Requirements File:

   • In the same folder, create a file named requirements.txt.

   • Add the following lines to specify the dependencies:

     openai
     mcp
     

4. Create a Dockerfile:

   • In the same folder, create a file named Dockerfile (no file extension).

   • Add the following content to define the Docker image:

     FROM python:3.11-slim
     
     WORKDIR /app
     
     COPY . .
     
     RUN pip install --no-cache-dir -r requirements.txt
     
     CMD ["python", "server.py"]
     

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🧪 Step 3: Build the Docker Image

1. Open Terminal:

   • Navigate to the codelabs-mcp-server directory using the cd command.

     cd path/to/codelabs-mcp-server
     

2. Build the Docker Image:

   • Run the following command to build your Docker image:

     docker build -t codelabs-mcp-server .
     

   • Docker will process the Dockerfile and create an image named codelabs-mcp-server.

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🧩 Step 4: Configure VS Code for MCP

1. Create Configuration Directory:

   • In your project directory (or the directory where students will work), create a folder named .vscode.

2. Create MCP Configuration File:

   • Inside the .vscode folder, create a file named mcp.json.

   • Add the following content to configure the MCP server:

     {
       "inputs": [
         {
           "type": "promptString",
           "id": "openai_api_key",
           "description": "Enter your OpenAI API Key",
           "password": true
         }
       ],
       "servers": {
         "codelabs-assistant": {
           "command": "docker",
           "args": [
             "run",
             "-i",
             "--rm",
             "-e",
             "OPENAI_API_KEY=${input:openai_api_key}",
             "codelabs-mcp-server"
           ]
         }
       }
     }
     

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🚀 Step 5: Run the MCP Server in VS Code

1. Open VS Code:

   • Launch Visual Studio Code.

2. Open the Project Folder:

   • Go to File > Open Folder and select the directory containing your project.

3. Access the Command Palette:

   • Press Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS) to open the Command Palette.

4. Start the MCP Server:

   • Type MCP: List Servers and select it.
   • In the list, find codelabs-assistant and click Start.

5. Enter OpenAI API Key:

   • When prompted, enter your OpenAI API key.
   • The server will start, and you can now interact with your custom GPT through VS Code's chat interface.

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By following these steps, you've successfully set up your custom GPT as a Dockerized MCP server, enabling your students to run and interact with it locally through Visual Studio Code. If you need further assistance or have any questions, feel free to ask!