How to Use the Confusion Matrix Engine MCP in OpenAI Agents SDK

Q: Does Confusion Matrix Engine support caching with OpenAI Agents SDK?

Yes. Set cacheToolsList=True in your connection parameters. This prevents the SDK from refetching the MCP tool definitions on every turn, saving you latency during high-volume classification runs.

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Connect Confusion Matrix Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect Confusion Matrix 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 Confusion Matrix Engine with OpenAI Agents SDK

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Stop OpenAI Agents SDK math hallucinations

LLMs fail at basic math, especially when calculating precision and recall on large datasets. This MCP Server lets your OpenAI agents offload those calculations to a deterministic engine instead of guessing. Calling `calculate_confusion_matrix` returns exact metrics instantly. You don't have to worry about your Python agents writing flaky pandas code on the fly. The tool returns true positives, false positives, precision, recall, F1-score, and accuracy without running arbitrary code.

Safe evaluation handoffs in production

When building multi-agent systems, you often need one agent to evaluate another's performance. This tool allows your supervisor agent to pass raw prediction arrays directly to the evaluation agent. The receiving agent triggers `calculate_confusion_matrix` to generate an objective scorecard. Because OpenAI's SDK supports built-in guardrails, you can validate the input arrays before they hit the calculation tool.

Trace evaluation runs in your dashboard

Debugging agentic evaluation pipelines is painful without visibility. By connecting this MCP Server to your setup, every call to `calculate_confusion_matrix` shows up directly in your OpenAI tracing dashboard. You see the exact input arrays and the resulting matrix. This deep integration makes it easy to spot where your classification models are tripping up without digging through raw terminal outputs or writing custom logging wrappers.

Setup guide

Set up Confusion Matrix 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 Confusion Matrix Engine tools at runtime.
3

Create your Agent

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

Spin up the MCP Server on Vinkius and grab your endpoint URL. Use `MCPServerStreamableHttp` to point to it, and pass that server object into your Agent constructor.

Yes. Set `cacheToolsList=True` in your connection parameters. This prevents the SDK from refetching the MCP tool definitions on every turn, saving you latency during high-volume classification runs.

Absolutely. You can define an evaluation agent that solely owns the `calculate_confusion_matrix` tool. Other agents route raw data to it whenever they need a classification run scored.

The tool throws an explicit error back to your agent. Instead of failing silently or guessing, your agent receives a clear error message so it can handle the data mismatch gracefully.

Your actual and predicted label arrays are processed entirely inside Vinkius's secure, ephemeral V8 sandboxes. No data is stored or logged after the mathematical calculations are finished.

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