How to Use the Metabolic Energy Estimator MCP in Vercel AI SDK

Q: How do I install the Metabolic Energy Estimator MCP Server for Vercel AI SDK?

Run npm install ai @ai-sdk/mcp in your project directory. Then, configure the client using createMCPClient with the server endpoint URL and pass the tools directly into streamText.

Q: How does the Metabolic Energy Estimator MCP Server handle connection cleanups in Vercel AI SDK?

You must call mcpClient.close() once your streaming session finishes. This prevents memory leaks on your Vercel Edge Functions by actively tearing down the transport layer.

Q: Can I use this server to estimate calories for custom exercises?

You must use searchactivitycatalog first to find a valid activity ID. The estimatecaloriesburned tool requires these predefined IDs to maintain mathematical accuracy based on clinical MET standards.

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Connect Metabolic Energy Estimator MCP to Vercel AI SDK

Create your Vinkius account to connect Metabolic Energy Estimator to Vercel AI SDK 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 Vercel AI SDK

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Claude

Perplexity

Stream TDEE calculations to Vercel AI SDK UIs

`calculate_tdee` processes raw user metrics using the Mifflin-St Jeor equation to output Basal Metabolic Rate and Total Daily Energy Expenditure instantly. Your Vercel AI SDK frontend receives this data point-by-point, letting you render metabolic baselines before the LLM finishes its complete thought. This means you bypass boring loading spinners. The client streams the exact caloric targets directly into custom UI cards, giving users immediate feedback on their metabolic baseline based on their selected activity multiplier.

Project weight loss timelines on the edge

`calculate_weight_loss_projection` maps out realistic timelines to reach target weights based on a 7,700-calorie deficit per kilogram of fat. It accounts for biological realities by projecting progress over weeks, giving your application a concrete data structure to display to the user. When paired with this MCP Server, Vercel AI SDK streams this projection directly into interactive charts. Users see their weight trajectory adjust dynamically as they tweak their daily calorie targets in the chat interface.

Search and calculate exercise burn in real-time

`search_activity_catalog` finds specific MET values from a local catalog of 80 activities to feed directly into your metabolic calculations. Once your client identifies the correct activity, it uses `estimate_calories_burned` to calculate the caloric output based on user weight and duration. Building this with Vercel AI SDK lets your users type natural queries like "I ran for 30 minutes" and watch the interface instantly update with the exact calorie burn. The local catalog query runs on the edge, keeping response times under 50 milliseconds.

Setup guide

Set up Metabolic Energy Estimator MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Metabolic Energy Estimator tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Metabolic Energy Estimator transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

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Common questions about Metabolic Energy Estimator MCP in Vercel AI SDK

Run `npm install ai @ai-sdk/mcp` in your project directory. Then, configure the client using `createMCPClient` with the server endpoint URL and pass the tools directly into `streamText`.

Yes. Because the server outputs structured data, you can use `streamUI` or tool calls to render dynamic charts as soon as the `calculate_weight_loss_projection` tool returns its payload.

You must call `mcpClient.close()` once your streaming session finishes. This prevents memory leaks on your Vercel Edge Functions by actively tearing down the transport layer.

You must use `search_activity_catalog` first to find a valid activity ID. The `estimate_calories_burned` tool requires these predefined IDs to maintain mathematical accuracy based on clinical MET standards.

All calculations for your weight, metabolic stats, and activity logs run inside a secure, sandboxed V8 isolate on this MCP Server. Vinkius processes these values ephemerally, meaning your personal health data is never written to disk or stored.

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