How to Use the Metabolic Energy Estimator MCP in Pydantic AI

Type-safe metabolic math for Pydantic AI agents using the Metabolic Energy Estimator.

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Connect Metabolic Energy Estimator MCP to Pydantic AI

Create your Vinkius account to connect Metabolic Energy Estimator to Pydantic AI 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 Pydantic AI

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Validate metabolic calculations at runtime

The `calculate_tdee` tool calculates Mifflin-St Jeor baselines and validates the output structure against Pydantic models in real-time. If your Pydantic AI agent receives malformed biometric metrics, the framework fails loudly, protecting your application from corrupt health data. This strict validation ensures your system never processes invalid weight, height, or age inputs. Your agent can safely use these validated TDEE outputs to generate precise daily caloric targets without risking silent runtime failures.

Enforce strict activity catalog lookups

The `search_activity_catalog` tool returns verified MET values from a local catalog of eighty activities, preventing your Pydantic AI agent from inventing exercises. Because the tool outputs are strictly typed, the agent must supply a valid activity ID to run `estimate_calories_burned`. This strict dependency keeps your calorie burn calculations deterministic. The agent cannot hallucinate MET values or activities, ensuring every logged workout is backed by verifiable metabolic equations.

Project weight loss with decay using this MCP Server

The `calculate_weight_loss_projection` tool models weight loss timelines by applying a dynamic decay factor to account for metabolic adaptation over time. Your Pydantic AI agent receives structured timeline data, making it easy to parse days and weeks to target weight. You configure this integration by passing the MCPToolset instance directly into your Agent constructor. The framework manages the underlying HTTP transport, giving your agent immediate, type-safe access to all four metabolic tools.

Setup guide

Set up Metabolic Energy Estimator MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "metabolic-energy-estimator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Metabolic Energy Estimator tools.",
)

result = await agent.run("List recent Metabolic Energy Estimator transactions")
print(result.output)

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Common questions about Metabolic Energy Estimator MCP in Pydantic AI

You instantiate an MCPToolset using the server's HTTP URL and pass it to your Agent's toolsets list. Pydantic AI automatically handles the tool registration and runtime validation for `calculate_tdee`.

The application raises a validation error immediately. This prevents your agent from using corrupt BMR, TDEE, or calorie burn numbers in downstream diet or workout planning.

Yes. You can use this MCP Server with OpenAI, Anthropic, Gemini, or local models. Pydantic AI handles the validation layer regardless of which LLM executes `calculate_weight_loss_projection`.

The projection tool on this MCP server applies a dynamic decay factor for projections over six weeks. This ensures your type-safe agent outputs realistic timelines rather than linear, unadjusted calculations.

This server operates statelessly inside an ephemeral V8 sandbox, meaning physical activity logs, weight, and height inputs are never stored. The data is processed in memory to run the calculation and instantly discarded.

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