Wine Tasting Visual Vocabulary

You can run the Wine Tasting Visual Vocabulary MCP Server to translate wine tasting vocabulary into visual parameters for AI image generation. This server lets you input sensory descriptors and receive structured visual vocabularies that guide image creation, making it practical to iterate and compare wine-inspired visuals across regions, varietals, and aging stages.

How to use

Use an MCP client to connect to the Wine Tasting Visual Vocabulary MCP Server. You can run the server locally, or access a cloud deployment if you have the deployment URL. Once connected, you can: create visual vocabularies from varietal, climate, winemaking style, oak treatment, and aging inputs; apply regional presets for classic wine regions; observe how visuals evolve over time; and compare profiles to explore visual contrasts between wines.

How to install

Prerequisites: you need Python and a functioning Python environment. You may also use shell access to clone repositories and install dependencies.

# Local development
cd wine_tasting_mcp
pip install -e .

Additional sections

Configuration options and deployment methods are described below. Choose the method that matches how you want to run the server: locally for development, or via a cloud deployment when you want a hosted MCP endpoint.

Local development and cloud deployment are demonstrated with concrete configurations and commands. The following examples show how to set up a local server, how to configure a desktop MCP client, and how to deploy to a cloud environment.

# Claude Desktop configuration (example JSON snippet)
{
  "mcpServers": {
    "wine-tasting": {
      "command": "python",
      "args": ["/path/to/wine_tasting_mcp/server.py"]
    }
  }
}

# Deploy to FastMCP Cloud
fastmcp deploy wine_tasting_mcp

# The server will be available at:
# https://[your-deployment].fastmcp.app/mcp

Tool usage and outputs

You will interact with a primary function that composes all categorical structures into a visual vocabulary. Typical usage involves specifying varietal, climate, winemaking style, oak treatment, aging, and balance-related parameters, and then receiving a structured visual vocabulary output that includes color, texture, composition, and atmospheric qualities.

Tips for using regional presets and evolution

Leverage regional presets to quickly set parameters for Burgundian reds, Napa Cabernets, Barolo, Mosel Riesling, and more. Use the evolution sequence to see how a wine’s visuals transform from youthful to past_prime, capturing the sense of aging in the image output.

Security and notes

Ensure you manage access to cloud endpoints and protect any API keys or deployment identifiers. When running locally, keep your server.py path and environment isolated from public access. Review environment variable usage and redact sensitive values in shared configurations.

Troubleshooting

If you encounter connection issues, verify that the MCP endpoint URL is correct, the local server process is running, and the client configuration points to the same server. Check for dependency installation errors and verify Python versions match the project requirements.