How to Use the Feature Scaler Engine MCP in Mastra AI

Run local data scaling inside your Mastra AI workflows without relying on slow external math libraries.

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Connect Feature Scaler Engine MCP to Mastra AI

Create your Vinkius account to connect Feature Scaler Engine to Mastra 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 Feature Scaler Engine with Mastra AI

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Branching workflows with Mastra AI and MCP

The `scale_features` tool scales your numeric columns using Z-Score or MinMax methods directly inside your pipeline. Mastra AI uses the resulting variance metrics to decide whether to route the data to a neural network or a clustering agent. You can build complex conditional steps based on the output. If a column shows high skew, your workflow can trigger alternative scaling parameters instantly.

Automated retries for failed scaling jobs

Mathematical accuracy is guaranteed when using the `scale_features` tool, but bad inputs can still happen. Mastra AI handles these exceptions using this local MCP Server to verify inputs and automatically retry with clean fallback parameters. This keeps your production data pipelines running without manual intervention. The workflow engine catches bad inputs, alerts your team, and proceeds with valid rows.

Local normalization without Python dependencies

Executing fast TypeScript-native scaling calculations inside your agent loops is easy with the `scale_features` tool. This setup eliminates the need to spin up heavy Python environments just to run basic Z-Score math. Your Mastra AI agents call the tool over a lightweight transport layer. You get clean, scaled inputs ready for model consumption in milliseconds.

Setup guide

Set up Feature Scaler Engine MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Feature Scaler Engine tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "feature-scaler-engine-mcp-client",
  servers: {
    "feature-scaler-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Feature Scaler Engine Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Feature Scaler Engine tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Feature Scaler Engine transactions"
);
console.log(result.text);

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Common questions about Feature Scaler Engine MCP in Mastra AI

Create an MCP client instance pointing to the Vinkius endpoint. Then, call `listTools` and spread the `scale_features` tool directly into your Mastra AI agent definition.

Yes, you can register the tool as a step in your workflow graph. This lets Mastra AI pass numeric tables through the scaler before feeding them to downstream models.

The tool throws clean errors for non-numeric values. Mastra AI catches these errors, allowing your workflow to filter out corrupt rows and retry the scaling process.

No, the scaling happens entirely in memory. It reads the raw array you provide and returns the normalized dataset instantly.

Yes, all numeric arrays are processed within an ephemeral V8 sandbox. No data is stored, and no external APIs are called during the math operations.

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