How to Use the Feature Scaler Engine MCP in Vercel AI SDK

Q: How do I configure Feature Scaler Engine with the Vercel AI SDK?

Install the @ai-sdk/mcp package and initialize the MCP client using the HTTP transport. Then, pass the scalefeatures tool directly into streamText or generateText inside your server action.

Stream numeric scaling operations directly into your Next.js frontend using the Vercel AI SDK and this local MCP Server.

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Connect Feature Scaler Engine MCP to Vercel AI SDK

Create your Vinkius account to connect Feature Scaler Engine 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 Feature Scaler Engine with Vercel AI SDK

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Fast scaling inside Vercel AI SDK streaming loops

The `scale_features` tool runs mathematical normalization locally on your numeric columns to prepare datasets for training. When your Vercel AI SDK client calls this MCP tool, the processed array bounds stream straight to the client without stalling the main thread. This means you bypass long waiting states. Your UI updates live as the data scales, letting users see the exact normalization shifts in real time.

Edge-ready Z-Score normalization

Running standard deviation and mean calculations without needing heavy external Python libraries is what the `scale_features` tool does best. Your edge functions running Vercel AI SDK can trigger these mathematical transformations instantly on incoming user CSVs. You get clean, zero-variance-protected outputs. Because the calculations run in a lightweight sandbox, your Next.js edge runtime stays fast and secure.

Direct UI updates for model inputs

Outputting structured JSON containing your scaled values and column metadata is the core function of the `scale_features` tool. Your Vercel AI SDK streams these tool results directly into your React components so charts render the scaled data immediately. You don't have to write custom API endpoints to clean the data. The agent handles the normalization step and updates the UI state in one clean cycle.

Setup guide

Set up Feature Scaler Engine 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 Feature Scaler Engine 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 Feature Scaler Engine transactions",
});

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

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Common questions about Feature Scaler Engine MCP in Vercel AI SDK

Install the `@ai-sdk/mcp` package and initialize the MCP client using the HTTP transport. Then, pass the `scale_features` tool directly into `streamText` or `generateText` inside your server action.

Yes, it processes numeric columns locally in memory. The tool sends the structured output back to your Vercel AI SDK client, which then streams the updated values straight to your React frontend.

Yes, the tool lets your Vercel AI SDK agent choose between Z-Score and MinMax methods. The agent analyzes the dataset shape and triggers the correct scaling mode automatically.

The server checks for zero variance before scaling. If a column contains only identical numbers, the tool returns a safe, unscaled array to prevent runtime crashes in your application.

Your numbers never leave the local environment. The server processes all scaling operations inside an isolated, zero-trust sandbox on Vinkius, ensuring your raw datasets remain completely private.

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