Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@colacloud-mcpFind recent wine labels from Napa Valley"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
colacloud-mcp
MCP server for COLA Cloud - access US alcohol label data from AI assistants.
Query 2.5M+ Certificate of Label Approval (COLA) records from the TTB (Alcohol and Tobacco Tax and Trade Bureau) directly from Claude, ChatGPT, or other MCP-compatible AI tools.
Installation
# Using uvx (recommended, no install required)
uvx colacloud-mcp
# Or install with pip
pip install colacloud-mcpConfiguration
Claude Desktop
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"colacloud": {
"command": "uvx",
"args": ["colacloud-mcp"],
"env": {
"COLA_API_KEY": "your-api-key-here"
}
}
}
}Other MCP Clients
Set the COLA_API_KEY environment variable and run:
export COLA_API_KEY="your-api-key-here"
colacloud-mcpGetting an API Key
Create an account at app.colacloud.us
Go to Dashboard > API Keys
Create a new API key
Free tier includes 500 requests/month.
Available Tools
Tool | Description |
| Search and filter COLA records by brand, product type, origin, dates, ABV |
| Get detailed info for a specific COLA by TTB ID |
| Search permit holders (alcohol producers/importers) |
| Get permit holder details with recent COLAs |
| Find COLAs by product barcode (UPC/EAN) |
| Check your API usage and rate limits |
Example Queries
Once configured, you can ask Claude things like:
"Search for Tito's vodka labels"
"Find wine labels from Napa Valley approved in 2024"
"Look up the product with barcode 080686001010"
"Show me recent labels from permit holder CA-I-123"
"What craft beers have an IBU over 60?"
Data Available
Each COLA record includes:
Basic info: Brand name, product name, product type, origin
Dates: Application, approval, expiration dates
Label images: Front, back, neck, strip labels (1-2 per record)
Extracted data: ABV, volume, barcodes (via OCR/computer vision)
AI enrichment: Category, tasting notes, product descriptions
Development
# Clone and install
git clone https://github.com/cola-cloud-us/colacloud-mcp
cd colacloud-mcp
uv sync --dev
# Run locally
export COLA_API_KEY="your-key"
uv run colacloud-mcp
# Run tests
uv run pytest
# Lint
uv run ruff check .License
MIT