How to Use the Feature Scaler Engine MCP in OpenAI Agents SDK

Use Feature Scaler Engine to normalize your data directly within your OpenAI Agents SDK production pipelines.

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Connect Feature Scaler Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect Feature Scaler Engine to OpenAI Agents 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 OpenAI Agents SDK

Ask AI about this MCP

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Claude

Perplexity

Deterministic data normalization

The `scale_features` tool forces your numeric inputs into a consistent range before they hit your model. It removes the variance that kills convergence in neural networks. Your agent calls this tool to perform Z-Score or MinMax math locally. You get clean, predictable tensors passed directly to your downstream inference logic.

Safe feature scaling for agents

Integration with OpenAI Agents SDK ensures your scaling logic stays behind your guardrails. Every operation is traced and validated before execution. You avoid the common trap of passing raw, unscaled data into your models. The `scale_features` tool ensures your agent only handles data that meets your mathematical requirements.

Managed MCP server performance

We cache the tool list to keep your agent startup times snappy. Your agent discovers `scale_features` on boot and keeps it ready for immediate use. This MCP server runs in our isolated sandbox, meaning your production environment stays clean. You manage the endpoint token while we handle the heavy lifting.

Setup guide

Set up Feature Scaler Engine MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Feature Scaler Engine tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Feature Scaler Engine tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Feature Scaler Engine tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Feature Scaler Engine Agent",
            instructions="You have access to Feature Scaler Engine tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Feature Scaler Engine MCP in OpenAI Agents SDK

You instantiate an MCPServerStreamableHttp object with your endpoint URL. Pass this server instance into your Agent constructor's mcp_servers list to enable tool discovery.

It processes data locally in memory. If your dataset fits in RAM, this MCP server will handle the scaling math without latency issues.

Yes, the tool outputs structured numeric data. Your agent receives exactly what it expects based on the schema defined by the engine.

The math still executes, but the results might skew your downstream model. We recommend checking your distribution before calling the tool.

Vinkius uses an ephemeral V8 sandbox for every request. We never store your numeric data, and your agent only sees the processed output.

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