ROC AUC Evaluator MCP for AI. Get mathematically precise model performance scores.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

ROC AUC Evaluator calculates the exact Area Under the ROC Curve for binary classification models. It runs complex statistical calculations locally, guaranteeing mathematically precise metrics that LLMs cannot compute.

Input true labels and predicted probability scores to validate your model's performance instantly.

What your AI can do

Calculate roc auc

Calculates the exact Area Under the ROC Curve for binary classification predictions using true labels and probability arrays.

Compute Model Comparison

Calculate and compare the ROC AUC scores for multiple models (Model A vs. Model B) run on the same dataset.

Determine Baseline Performance

Test if a model performs better than random chance by comparing its calculated AUC score against 0.5.

Calculate Exact AUC Score

Generate the mathematically precise Area Under the ROC Curve for any given set of true labels and probability scores.

Validate Classification Metrics

Receive reliable, industry-standard performance metrics needed for model deployment decisions.

Ask an AI about this

Included with Plan

Waiting for input…

AI Agent

ROC AUC Evaluator MCP Server: 1 Tool for ML Evaluation

Use this server's tools to compute exact, reliable performance metrics like the Area Under the ROC Curve (AUC) for any binary classification model.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using ROC AUC Evaluator on Vinkius

Calculate Roc Auc

Calculates the exact Area Under the ROC Curve for binary classification predictions using true labels and probability arrays.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The ROC AUC Evaluator integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "roc-auc-evaluator": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the ROC AUC Evaluator tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"roc-auc-evaluator": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with ROC AUC Evaluator, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Native V8. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Calculating model metrics used to be a manual mess.

Before specialized tools, calculating the ROC AUC score meant exporting data into Excel or running custom Python scripts in Jupyter notebooks. You had to manually handle probability sorting and calculate cumulative true positive rates—a tedious process prone to copy-paste errors and dependency conflicts.

Now, your agent handles it. You feed the labels and probabilities directly through `calculate_roc_auc` via the MCP Server. It returns one number: the definitive AUC metric, instantly.

The ROC AUC Evaluator MCP Server: Calculate model metrics without leaving chat.

You no longer need to switch context between your IDE and a separate analytics tool. The server accepts raw data arrays—labels and probabilities—and executes the complex trapezoidal integration locally, all within your existing workflow.

The result is immediate: mathematically rigorous proof of model performance right where you're working.

Support 24/7 support@vinkius.com ↗

Security Vinkius Trust Center ↗

SLA Service Level Agreement ↗

Report Listing Send Report ↗

What your AI can actually do with this

Forget what those big language models spit out; they can't do this math right. You need to know exactly how good your classification model is, and that means calculating the Area Under the ROC Curve (AUC). The calculate_roc_auc tool handles the heavy lifting: it computes the mathematically exact AUC score for binary predictions using both your true labels and your predicted probability arrays.

This isn't a rough estimate; this is rigorous statistical work.

The core function lets you feed in your test set labels and the corresponding probability scores from any model. It then runs complex calculations locally, guaranteeing that the resulting metrics are mathematically precise—something standard chatbots just can’t compute from raw data arrays. You're getting industry-standard performance metrics right out of the gate, which is exactly what you need before you decide to deploy anything.

You ain't limited to checking one model, either. Wanna see if Model A blows Model B out of the water? The system lets you calculate and compare the ROC AUC scores for multiple models running against the same dataset. You can pit them against each other right here and figure out which one’s actually got the edge.

Need to know if your fancy new model is even better than flipping a coin? No sweat. You just run the calculation and check it against 0.5. If your AUC score dips below that threshold, you're basically guessing, period. This tool helps you determine baseline performance immediately by comparing its calculated AUC score directly to random chance.

When you need reliable metrics for high-stakes decisions—like deciding which model passes testing or needs a complete overhaul—you use this server. It generates the precise Area Under the ROC Curve required for serious validation. You're getting clean, trustworthy data points that tell you exactly how well your classifier distinguishes between classes based on those probability outputs.

Built · Hosted · Managed by Vinkius ROC AUC Evaluator - Compute Model Performance Metrics

Server ID 019e38e5-8fb3-71d5-b27e-42ce25290c5f

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

Here's how it actually works

The bottom line is: it gives you mathematically verifiable model scores without relying on LLM estimation.

Provide the tool with two datasets: the true binary outcomes (labels) and the predicted probability scores generated by your classification model.

Your AI client invokes the calculate_roc_auc function, which executes the computation using a local Node.js process for guaranteed mathematical precision.

The server returns the exact AUC metric, allowing you to quantitatively compare models or check performance against a defined baseline.

Who is this actually for?

Data Scientists and ML Engineers who need to prove their models are reliable before deployment. This tool stops the pain of manual, error-prone calculations and guesswork. If you’re tired of running validation checks in Jupyter notebooks just to get a single, precise number, this is for you.

Machine Learning Engineer

Uses calculate_roc_auc to integrate model performance testing into CI/CD pipelines and ensure models pass required AUC thresholds.

Data Scientist

Compares the ROC AUC scores of multiple candidate models (Model A vs. Model B) to select the optimal architecture for a new feature.

Quant Analyst

Validates classification metrics against established industry benchmarks, ensuring performance exceeds random chance (AUC > 0.5).

See it in action

01 01

Comparing two competing model versions.

You have Model A and Model B, both trained on the same data. Instead of running a comparison script locally, you ask your agent to use calculate_roc_auc with probability arrays for both models. It returns two distinct AUC scores, letting you tell management which version is superior.

02 02

Proving model efficacy in a new domain.

You trained a model to spot fraud, but it needs to perform better than random guessing (AUC > 0.5). You use calculate_roc_auc on your test data. If the score is below 0.5, you know immediately that the model needs major retraining.

03 03

Building a robust ML validation pipeline.

Your CI/CD process requires that any new model must achieve an AUC of at least 0.8. You connect your agent to use calculate_roc_auc as the final check step, failing the build if the metric falls below the required threshold.

The honest tradeoffs

Asking the AI client for an estimate.

Anti-pattern

Prompting your agent: 'What is the ROC AUC score here?' The LLM will attempt to calculate it based on pattern matching, which is mathematically inaccurate and unreliable.

The Fix

Always use calculate_roc_auc. You must explicitly pass both true labels and predicted probabilities to the tool. This forces the calculation through a local Node.js process that handles the complex math correctly.

Using simple counts for performance.

Anti-pattern

Relying only on Accuracy (e.g., 90% correct) misses critical information about class imbalance or threshold sensitivity, leading to false confidence in a model.

The Fix

Use calculate_roc_auc. This metric accounts for the entire probability distribution, giving you a single value that describes overall separability—a much deeper measure than simple accuracy.

When It Fits, When It Doesn't

Use this server if your primary requirement is to calculate the exact AUC score from raw data. If you need to compare multiple models or check against a statistical baseline (like 0.5), this tool is essential. Don't use it if you only need basic descriptive statistics, like counting the total number of positive predictions; for that, standard array tools work fine. However, if your goal involves quantifying model performance across probability thresholds—the core job of ROC AUC—then calculate_roc_auc is non-negotiable.

Questions you might have

Why is calculating AUC difficult for LLMs? +

AUC requires sorting an array of probabilities, stepping through each threshold, and integrating the True Positive Rate over the False Positive Rate. LLMs cannot perform reliable array sorting or integral math.

What format should the probabilities be in? +

Provide a JSON array of actual labels (0 or 1) and a matching JSON array of predicted probabilities (floats between 0.0 and 1.0).

Is this identical to Python's scikit-learn AUC? +

Yes, it uses the identical trapezoidal rule approach to compute the area under the curve deterministically.

If I use the `calculate_roc_auc` tool with mismatched input array sizes, how does it handle the error? +

The server validates all inputs immediately. It throws an explicit error detailing which arrays mismatch in size. This stops inaccurate calculations and lets you debug your data preparation step right away.

How efficient is `calculate_roc_auc` when I run it on very large test sets? +

The computation runs locally using Node.js, making it highly stable for big data. Performance scales linearly with the input size (N), providing quick and reliable results even across tens of thousands of records.

What environment setup or dependencies are required to run `calculate_roc_auc`? +

This server requires a Node.js v8 runtime, as noted by its Native V8 integration. Your AI client must be configured with access to this specific JavaScript execution environment for the tool to function correctly.

Can I run `calculate_roc_auc` if I only provide probability scores without true binary labels? +

No. The tool requires both the array of predicted probabilities and the corresponding ground truth outcomes. Both sets are mandatory because AUC calculation depends on pairing predictions with known correct answers.

When I execute `calculate_roc_auc`, what specific metrics does the output provide? +

The tool returns a single, precise floating-point number: the final AUC score. It does not give intermediate values or curves; it provides only the exact metric for direct reporting and model comparison.

Connect to your AI in seconds.

Calculate roc auc

ROC AUC Evaluator MCP Server: 1 Tool for ML Evaluation

Make your AI actually useful.

Calculate Roc Auc

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Calculating model metrics used to be a manual mess.

The ROC AUC Evaluator MCP Server: Calculate model metrics without leaving chat.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Comparing two competing model versions.

Proving model efficacy in a new domain.

Building a robust ML validation pipeline.

The honest tradeoffs

Asking the AI client for an estimate.

Using simple counts for performance.

When It Fits, When It Doesn't

Questions you might have