How to Use the Temperature Converter for Cooking MCP in Google ADK

Feed exact oven temperatures into your Google ADK pipelines for accurate culinary data processing.

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Connect Temperature Converter for Cooking MCP to Google ADK

Create your Vinkius account to connect Temperature Converter for Cooking to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Temperature Converter for Cooking with Google ADK

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Process Recipes with Google ADK

Your agent needs reliable heat data to standardize international recipes stored in BigQuery. This MCP exposes `celsius_from_fahrenheit` and `fahrenheit_from_celsius` to normalize your datasets. You also get `oven_settings_from_gas` to translate analog European dials into structured digital records. You pass the toolset object directly to your LlmAgent. The agent reads a million tokens of raw cookbook text, extracts the cooking instructions, and runs the conversions in bulk. The results pipe straight back into your Vertex AI workflow.

Filter Your Connection Tools

Enterprise deployments require strict access controls. You restrict exposed functions using the optional tool names filter in your setup configuration. If your agent only needs metric conversions, you block the gas dial tool entirely. Setup requires the google-adk package and a streamable HTTP configuration. The framework handles the transport automatically so your agent can focus on processing culinary data instead of managing network connections.

Long-Context Culinary Logic

Complex food science requires deep context windows. Your agent analyzes entire historical cookbooks and cross-references them with modern food safety guidelines. It uses this MCP to verify that a historical low-temperature bake meets current USDA standards. The agent holds the entire conversion history in memory. It applies heat intensity classifications across thousands of recipes without losing track of the original gas mark inputs. You get mathematically sound outputs ready for production.

Setup guide

Set up Temperature Converter for Cooking 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 Temperature Converter for Cooking 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="Temperature Converter for Cooking_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Temperature Converter for Cooking tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Temperature Converter for Cooking MCP in Google ADK

Install google-adk. Define your Vinkius endpoint using the streamable HTTP parameters and pass the resulting toolset to your LlmAgent.

Yes. Your agent can read recipe rows from BigQuery, convert the thermal metrics using this MCP, and write the standardized integers back to your database.

It does. You can restrict the agent to specific metric functions by passing an array of allowed names during the toolset initialization.

The long-context window allows the agent to ingest massive culinary texts, spot analog dial references, and map them to precise digital degrees using this integration.

The connection only transmits raw numeric floats and short dial strings. Your proprietary cookbook datasets stay entirely within your cloud infrastructure.

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