How to Use the Chi-Square Test Engine MCP in OpenAI Agents SDK

Stop letting OpenAI Agents SDK hallucinate math by connecting this MCP server for direct, CPU-backed Chi-Square calculations.

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Connect Chi-Square Test Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect Chi-Square 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 Chi-Square Test Engine with OpenAI Agents SDK

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Stop math hallucinations in OpenAI Agents SDK

The `calculate_chi_square` tool gives your OpenAI agents a direct pipeline to exact statistical math instead of letting them guess numbers. LLMs are notoriously bad at division and matrix math, which ruins contingency table analysis. This tool bypasses the LLM's neural net entirely to run the math on dedicated CPU cores. By declaring this tool inside your agent setup, your assistant detects the tool automatically. It feeds the raw contingency table directly to the engine and gets back exact p-values. No more raw guessing, just pure Python-native math returned to your agent runtime.

Safe statistical guardrails for your production agents

The `calculate_chi_square` tool runs within a secure sandboxed environment to keep your production agent pipeline stable. Because OpenAI Agents SDK supports runtime guardrails, you can intercept the tool call before execution to ensure the matrix dimensions match your specs. This MCP setup prevents rogue agents from submitting massive, broken datasets that waste compute. You get clean, validated inputs going in and exact deterministic test statistics coming out.

Full tracing for MCP Server operations

The `calculate_chi_square` tool logs every single contingency table execution directly to your OpenAI dashboard. When your multi-agent system hands off a dataset from a processing agent to an analyst agent, you can trace the exact payload. Debugging failed runs becomes trivial because you see the exact input matrix and the resulting p-value. This visibility ensures your production statistical pipelines remain auditable and clear.

Setup guide

Set up Chi-Square 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 Chi-Square Test Engine tools at runtime.
3

Create your Agent

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

Install the SDK and use the streamable HTTP server class to point to the Vinkius endpoint. Pass this server instance directly to your Agent constructor inside the mcp_servers list. Your agents will instantly discover the tool.

Yes, the SDK handles concurrent runs out of the box. The underlying engine processes multiple contingency tables simultaneously without blocking your agent's execution loop.

Absolutely. Every call to the tool gets logged with its full input matrix and statistical output. You can monitor latency and inspect payloads directly in your run history.

The MCP Server returns a clean error message detailing the issue, like unequal row lengths or negative values. Your agent reads this error directly and can correct its input on the next turn.

All contingency tables sent for testing are processed in an ephemeral sandbox that destroys the memory space immediately after returning the p-value. Vinkius runs this in a zero-trust isolate, meaning your raw matrix counts are never saved to disk or used for training.

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