How to Use the Metabolic Energy Estimator MCP in Google ADK

Q: How does the tool handle long-term metabolic adaptation?

The calculateweightlossprojection tool applies a dynamic decay coefficient for projections exceeding six weeks. This ensures your Gemini agent does not generate overly optimistic timelines that ignore metabolic slowdown.

Q: Is user weight and height data sent to third parties?

No. All calculations for calculatetdee occur locally within the secure, zero-trust Vinkius container. Your sensitive biometric data never leaves your Google Cloud and Vinkius network boundary.

Fuel Gemini health agents with Metabolic Energy Estimator math directly inside the Google ADK.

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Connect Metabolic Energy Estimator MCP to Google ADK

Create your Vinkius account to connect Metabolic Energy Estimator 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 Metabolic Energy Estimator with Google ADK

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Feed BigQuery biometric data into metabolic calculations

The `calculate_tdee` tool calculates Mifflin-St Jeor metabolic baselines using biometric datasets pulled directly from your BigQuery tables. By combining Google ADK with this MCP server, your Gemini agent reads user height, weight, and age, then outputs precise daily calorie targets. This direct data pipeline eliminates manual entry errors. Your agent processes thousands of user profiles in batches, writing the calculated BMR and TDEE values back to your cloud database for long-term health tracking.

Run MET-based burn calculations over long contexts

The `search_activity_catalog` tool searches eighty local activities to find precise MET values for your Google ADK agent. Your agent can ingest months of raw workout logs in a single 1M-token context window, matching each entry to catalog IDs to calculate energy expenditure via `estimate_calories_burned`. Doing this locally avoids rate limits and costly external API calls. The agent processes hours of physical activity records in seconds, outputting exact calorie burns based on deterministic metabolic formulas.

Project weight loss timelines using this MCP Server

The `calculate_weight_loss_projection` tool computes weight loss timelines using a strict 7700-calorie-per-kilogram ratio, adjusting for metabolic decay over long horizons. Your Google ADK agent uses these calculations to build realistic fitness plans that respect biological plateaus. You can restrict exposure to this specific tool using the tool_names filter in your toolset configuration. This keeps your Gemini agent focused solely on timeline projections while ignoring other fitness calculations when necessary.

Setup guide

Set up Metabolic Energy Estimator 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 Metabolic Energy Estimator 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="Metabolic Energy Estimator_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Metabolic Energy Estimator tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Metabolic Energy Estimator MCP in Google ADK

You initialize the McpToolset with the server HTTP transport URL and pass it to the LlmAgent constructor. The Google ADK automatically maps `calculate_tdee` and the other MCP tools to your Gemini model.

Yes. Your Google ADK agent reads raw activity text from BigQuery, uses `search_activity_catalog` to find matching activities, and then computes the energy burn via `estimate_calories_burned`.

Yes, the server runs in a secure, ephemeral V8 sandbox designed for high-concurrency enterprise workloads. Your Gemini agent can execute thousands of MCP server calls simultaneously.

The `calculate_weight_loss_projection` tool applies a dynamic decay coefficient for projections exceeding six weeks. This ensures your Gemini agent does not generate overly optimistic timelines that ignore metabolic slowdown.

No. All calculations for `calculate_tdee` occur locally within the secure, zero-trust Vinkius container. Your sensitive biometric data never leaves your Google Cloud and Vinkius network boundary.

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