How to Use the Normality Test Engine MCP in OpenAI Agents SDK

Stop letting agents hallucinate math. Run deterministic Jarque-Bera tests directly inside your OpenAI Agents SDK production pipelines.

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Connect Normality Test Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect Normality Test 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 Normality Test Engine with OpenAI Agents SDK

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Deterministic math for OpenAI Agents SDK

The `test_normality` tool forces your agent to stop guessing and calculate exact skewness and kurtosis. Language models cannot do reliable arithmetic on large arrays. When you connect this MCP Server to your production setup, you offload the actual statistical computation to a deterministic engine. Your agent hands over the numeric array, and the server runs a Jarque-Bera test. The OpenAI dashboard traces the exact inputs and outputs. If the data fails the normality check, your agent can safely hand off the workflow to a non-parametric analysis specialist agent.

Guardrailed statistical pre-checks

Running parametric tests on skewed data guarantees false discoveries. This tool acts as a hard gatekeeper before your agent touches any t-test or ANOVA. You enforce this check using the built-in guardrails of your agent framework. Set `cacheToolsList=True` in your initialization, and your agent discovers the tool instantly. It evaluates the numeric data, returns the p-value, and blocks invalid down-stream assumptions. You get math that actually works in production.

Async execution for production workflows

Blocking the main thread while calculating kurtosis on massive datasets kills performance. You wrap your MCP Server connection in an async context manager, keeping your application responsive while the engine crunches the numbers. The connection remains stable across complex multi-agent interactions. One agent formats the numeric data, passes it to the tool, and routes the validated output to a reporting agent. You stop worrying about silent math failures in your environment.

Setup guide

Set up Normality Test 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 Normality Test Engine tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Normality Test 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 Normality Test 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="Normality Test Engine Agent",
            instructions="You have access to Normality Test 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 Normality Test Engine MCP in OpenAI Agents SDK

Install the openai-agents package. Initialize MCPServerStreamableHttp with your endpoint URL and pass it to your Agent constructor using the mcp_servers list.

Agents write code that fails at runtime or hallucinates outputs. The test_normality tool provides a guaranteed, pre-tested Jarque-Bera implementation that executes instantly.

Yes. If the data fails the normality test, you can configure your agent's guardrails to halt execution or pivot to non-parametric methods automatically.

You pass the numeric data array directly to the tool. The deterministic engine calculates skewness and kurtosis locally, bypassing the token limits of the language model context window.

This MCP Server only touches the raw numeric arrays you send it. The engine calculates the Jarque-Bera statistics in memory and drops the raw floats immediately after returning the metrics.

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