mcp-learning

Model Context Protocol (MCP)

All credits to : https://github.com/ShawhinT/YouTube-Blog/

Fourth example in AI agents series. Here, I build a customer MCP server to give any AI app access to a toolset for an Artificial Virtual Assistant (AVA).

Links

• Video
• Blog

How to run this example

1. Clone this repo
2. Install uv if you haven't already

# Mac/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

3. Test the server in dev mode

uv run mcp dev mcp-server-example.py

4. Add server config to AI app (e.g. Claude Desktop or Cursor).

{
  "mcpServers": {
    "AVA": {
      "command": "/Users/shawhin/.local/bin/uv", # replace with global path to your uv installation
      "args": [
        "--directory",
        "/Users/shawhin/Documents/_code/_stv/sandbox/ava-mcp/", # replace with global path to repo
        "run",
        "mcp-server-example.py"
      ]
    }
  }
}

Customizing AVA's Behavior

Update Personal Details and Preferences

1. Locate the prompts/ava.md file in your project directory
2. Customize the file with:
   • Communication preferences
   • Specific instructions for handling tasks
   • Any other relevant guidelines for AVA

Environment Setup (.env)

1. Create a .env file in the root directory of the project with the following variables:

USER_EMAIL=your_email_address

# Google OAuth Credentials
GOOGLE_CREDENTIALS_PATH=.config/ava-agent/credentials.json
GOOGLE_TOKEN_PATH=.config/ava-agent/token.json

Required Environment Variables:

• USER_EMAIL: The Gmail address you want to use for this application
• GOOGLE_CREDENTIALS_PATH: Path to your Google OAuth credentials file
• GOOGLE_TOKEN_PATH: Path where the Google OAuth token will be stored

Google OAuth Setup

1. Create Project Directory Structure

First, create the required directory structure:

mkdir -p .config/ava-agent

2. Set Up Google Cloud Project

1. Go to the Google Cloud Console
2. Create a new project or select an existing one
3. Enable the Gmail API:
   • In the navigation menu, go to "APIs & Services" > "Library"
   • Search for "Gmail API"
   • Click "Enable"

3. Create OAuth Credentials

1. In the Google Cloud Console:

   • Go to "APIs & Services" > "Credentials"
   • Click "Create Credentials" > "OAuth client ID"
   • Choose "Desktop application" as the application type
   • Give it a name (e.g., "AVA Gmail Client")
   • Click "Create"

2. Download the credentials:

   • After creation, click "Download JSON"
   • Save the downloaded file as credentials.json in .config/ava-agent/
   • The file should contain your client ID and client secret

4. Configure OAuth Consent Screen

1. In the Google Cloud Console:
   • Go to "APIs & Services" > "OAuth consent screen"
   • Choose "External" user type
   • Fill in the required information:
     • App name
     • User support email
     • Developer contact information
   • Add the Gmail API scope: https://www.googleapis.com/auth/gmail.modify
   • Add your email as a test user
   • Complete the configuration

Signing into Google

Before the server can access you Gmail account you will need to authorize it. You can do this by running uv run oauth.py which does the following.

1. Check for the presence of token.json
2. If not found, it will initiate the Google OAuth authentication flow
3. Guide you through the authentication process in your browser:
   • You'll be asked to sign in to your Google account
   • Grant the requested permissions
   • The application will automatically save the token
4. Generate and store the token automatically

Security Notes

File Protection

• Never commit your .env file or token.json to version control
• Keep your Google credentials secure
• Add the following to your .gitignore:

  .env
  .config/ava-agent/token.json
  .config/ava-agent/credentials.json