ROC AUC Evaluator MCP Server for OpenAI Agents SDKGive OpenAI Agents SDK instant access to 1 tools to Calculate Roc Auc
GDPR Free for SubscribersThe OpenAI Agents SDK enables production-grade agent workflows in Python. Connect ROC AUC Evaluator through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
Ask AI about this MCP Server for OpenAI Agents SDK
The ROC AUC Evaluator MCP Server for OpenAI Agents SDK is a standout in the Developer Tools category — giving your AI agent 1 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="ROC AUC Evaluator Assistant",
instructions=(
"You help users interact with ROC AUC Evaluator. "
"You have access to 1 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from ROC AUC Evaluator"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About ROC AUC Evaluator MCP Server
The Area Under the Receiver Operating Characteristic Curve (ROC AUC) is a vital metric for evaluating binary classification models. Because it involves sorting probabilities and integrating the area under a curve iteratively, Large Language Models are mathematically incapable of calculating exact AUC scores from raw arrays. The ROC AUC Evaluator offloads this task to local Node.js processes, instantly returning mathematically rigorous AUC metrics using the exact trapezoidal rule.
The OpenAI Agents SDK auto-discovers all 1 tools from ROC AUC Evaluator through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries ROC AUC Evaluator, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
The ROC AUC Evaluator MCP Server exposes 1 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 1 ROC AUC Evaluator tools available for OpenAI Agents SDK
When OpenAI Agents SDK connects to ROC AUC Evaluator through Vinkius, your AI agent gets direct access to every tool listed below — spanning binary-classification, model-evaluation, mathematical-computation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Calculate roc auc on ROC AUC Evaluator
Calculates the exact Area Under the ROC Curve (AUC) for binary classification
Connect ROC AUC Evaluator to OpenAI Agents SDK via MCP
Follow these steps to wire ROC AUC Evaluator into OpenAI Agents SDK. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install the SDK
pip install openai-agents in your Python environmentReplace the token
[YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.comRun the script
python agent.pyExplore tools
Why Use OpenAI Agents SDK with the ROC AUC Evaluator MCP Server
OpenAI Agents SDK provides unique advantages when paired with ROC AUC Evaluator through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
ROC AUC Evaluator + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the ROC AUC Evaluator MCP Server delivers measurable value.
Automated workflows: build agents that query ROC AUC Evaluator, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries ROC AUC Evaluator, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through ROC AUC Evaluator tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query ROC AUC Evaluator to resolve tickets, look up records, and update statuses without human intervention
Example Prompts for ROC AUC Evaluator in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with ROC AUC Evaluator immediately.
"I have true binary outcomes and the predicted probability scores from my model. Calculate the exact ROC AUC score."
"Here are 50 true labels and 50 probabilities. Can you use the ROC evaluator and tell me if my model performs better than random guessing (AUC > 0.5)?"
"I have probability arrays for Model A and Model B for the same actual test set. Find the AUC for both and tell me which one is superior."
Troubleshooting ROC AUC Evaluator MCP Server with OpenAI Agents SDK
Common issues when connecting ROC AUC Evaluator to OpenAI Agents SDK through Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
ROC AUC Evaluator + OpenAI Agents SDK FAQ
Common questions about integrating ROC AUC Evaluator MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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