How to Use the Cocktail API MCP in Google ADK

Q: Can Gemini use Cocktail API to write new recipes into BigQuery?

Gemini can pull recipes using getrumcocktails and then use standard Google ADK tools to write those structured JSON objects directly into your BigQuery dataset.

Run large-scale menu analysis on Google ADK with direct access to verified cocktail recipes.

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Connect Cocktail API MCP to Google ADK

Create your Vinkius account to connect Cocktail API to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cocktail API with Google ADK

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BigQuery menu matching via Google ADK

This Google ADK toolset connects your database to the Cocktail API MCP Server. Gemini models then cross-reference your current bar inventory tables against recipe metrics from `get_cocktails_by_ingredients`. Your enterprise agent analyzes thousands of rows of ingredient stock. It then calls `get_tequila_cocktails` to suggest high-margin menu additions based on what you already have in storage.

Long-context recipe auditing

This MCP server allows enterprise agents to audit entire recipe books against standard industry definitions. Feed entire recipe books into Gemini's million-token context window to spot errors. Instead of struggling with short context limits, your agent reads the whole library. It calls `get_classic_martinis` to check if your historical specs match standard industry definitions.

Restricted toolsets for enterprise deployments

This deployment configuration restricts your agent's capabilities to prevent unwanted operations on the floor. Pass a filtered tool list to the MCP toolset to expose only `get_gin_cocktails` or `get_vodka_cocktails` to public-facing kiosks. Keeping the toolset narrow keeps your cloud costs predictable and prevents unauthorized calls. Your agent stays focused on serving customers drink profiles instead of querying internal database structures.

Setup guide

Set up Cocktail API MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Cocktail API tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Cocktail API_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Cocktail API tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Cocktail API MCP in Google ADK

Instantiate McpToolset with the streamable HTTP URL provided by Vinkius. Pass that toolset directly to your LlmAgent in your Python code, allowing Gemini to instantly access `get_classic_margaritas` via MCP.

Yes, it supports both. For cloud deployments on Cloud Run, we recommend using the Streamable HTTP transport to keep your architecture serverless and scale-to-zero.

Gemini can pull recipes using `get_rum_cocktails` and then use standard Google ADK tools to write those structured JSON objects directly into your BigQuery dataset.

Gemini holds your entire bar history in memory. When it calls `search_cocktails`, it compares the results against your historical preferences to recommend the exact gin variation your guests prefer.

Every request running through this MCP server runs in an isolated, zero-trust container. No data is stored on Vinkius disks, ensuring your competitive menu advantages and search queries remain strictly confidential.

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