How to Use the Egg Boiling Timer MCP in Google ADK

Connect the Egg Boiling Timer to Google ADK for enterprise-grade culinary calculations.

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Connect Egg Boiling Timer MCP to Google ADK

Create your Vinkius account to connect Egg Boiling Timer 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 Egg Boiling Timer with Google ADK

Ask AI about this MCP

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Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

BigQuery meets kitchen reality

Your AI client needs hard facts to cook properly. This MCP replaces guesswork with strict physical rules. When you require exact cooking durations, `calculate_boiling_time` does the heavy lifting. It takes the variables and spits out a hard number. Watch the clock.

Catching bad inputs early

Here's the thing. Users lie about their ingredient sizes. Run `validate_egg_parameters` to stop bad math before it hits your Vertex AI pipelines. If someone claims they have a two-pound chicken egg, the system rejects it. Keep it simple.

Aligning on texture definitions in your MCP Server

Subjective terms cause kitchen disasters. We rely on strict definitions. Trigger `get_texture_descriptions` to map vague requests to actual physical states. A hard boil means a fully set yolk. That's the science of it.

Setup guide

Set up Egg Boiling Timer 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 Egg Boiling Timer 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="Egg Boiling Timer_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Egg Boiling Timer tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Egg Boiling Timer. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Egg Boiling Timer MCP in Google ADK

Use the McpToolset class. Point the server parameters to your HTTP endpoint. Pass the resulting object to your LlmAgent.

Look, you can restrict access. Pass a specific list to the tool names parameter. The agent will ignore the rest.

The context window size doesn't matter for basic math. The agent reads the tool output and continues its reasoning process.

It supports both standard input/output and HTTP transports. Pick the one that fits your deployment architecture.

We process target doneness levels and initial temperatures. The connection drops the payload immediately after calculating the duration. Your enterprise data never persists.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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