How to Use the BigOven MCP in Google ADK

Connect Gemini's massive context to BigOven recipes using the Google ADK and our managed MCP Server.

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Connect BigOven MCP to Google ADK

Create your Vinkius account to connect BigOven 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 BigOven with Google ADK

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Deep Recipe Analysis on Google ADK

Using the `get_recipe` tool retrieves exhaustive culinary details including ingredient lists, preparation steps, and nutritional profiles. By passing this structured data into Gemini's million-token context window, your agent can analyze complex cooking techniques across hundreds of dishes simultaneously. You can combine this with BigQuery datasets to cross-reference user pantry inventories with recipe requirements. The SDK makes it simple to feed these rich datasets directly into your Vertex AI workflows.

Structured Ingredient Searches

Your agent executes the `search_by_ingredient` tool to find matching recipes based on specific food items your agent extracts from user inputs. This MCP Server lets your Google Cloud agent run targeted queries without manual API integration. If your model needs to refine its search, it calls `list_categories` to map user preferences to valid culinary classifications. This ensures your agent never queries invalid endpoints.

Curated Menu Planning

Calling the `list_collections` tool retrieves themed and seasonal recipe groups curated by culinary experts. Your enterprise agent uses these collections to generate structured weekly meal plans for your users. After identifying a collection, the agent calls `get_collection` to extract individual recipe structures. The SDK handles the underlying transport, leaving you to focus on prompt engineering.

Setup guide

Set up BigOven 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 BigOven 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="BigOven_agent",
    model="gemini-2.0-flash",
    instruction="You have access to BigOven tools via MCP.",
    tools=mcp_tools,
)

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Common questions about BigOven MCP in Google ADK

You register the server using the HTTP streamable parameters class and pass it to your LlmAgent. The Google ADK automatically maps the recipe tools to Gemini's function calling interface.

Yes, your agent can query BigQuery for user inventory and then call `search_by_ingredient` to find matching meals. The SDK manages both data sources within a single conversational loop.

Gemini's long context easily digests large outputs from tools like `get_recipe_reviews` or detailed collections. You do not need to worry about aggressive truncation or token limits.

Yes, the toolset constructor accepts a filter parameter where you specify allowed tool names. If you only need search functions, expose only `search_recipes` and `search_by_ingredient`.

The MCP connection utilizes an ephemeral, zero-trust sandbox environment on Vinkius. Your search queries, recipe IDs, and user preferences are never used to train models or shared outside your Google Cloud infrastructure.

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