Vinkius
Deterministic Quiz Scorer

Deterministic Quiz Scorer MCP for AI. Grade complex quizzes with deep metric analysis.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Connect to your AI in seconds.

Deterministic EdTech Quiz Scorer automatically grades quizzes by cross-referencing user answers against weighted answer keys. It generates deep performance metrics instantly, providing category weakness breakdowns and calculating average time per question for detailed student analysis.

What your AI can do

Score quiz

Cross-references a user's submitted answers against weighted keys and generates detailed EdTech metrics, including category accuracy percentages.

Calculate weighted final scores

The MCP automatically compares provided answers to an answer key while applying custom point values to generate a total score.

Identify category weaknesses

It breaks down the overall exam result by specified subject categories, pinpointing exactly which area needs more focus.

Determine performance percentage

The tool calculates and reports the student's accuracy as a precise percentage relative to the maximum possible score.

Track average time metrics

By accepting total completion time, it derives the critical metric showing how many seconds were spent on each question.

Included with Plan

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AI Agent

Deterministic EdTech Quiz Scorer: 1 Tool

Use the available tools to calculate detailed performance metrics by cross-referencing quiz answers against weighted keys.

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 Deterministic EdTech Quiz Scorer on Vinkius

Score Quiz

Cross-references a user's submitted answers against weighted keys and generates detailed EdTech metrics, including category accuracy...

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Claude AI

Claude AI

1

Open Claude Settings

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

2

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.

3

Start a conversation

Open a new chat. The Deterministic Quiz Scorer integration is available immediately — no restart needed.

Choose How to Get Started

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Start with Deterministic EdTech Quiz Scorer, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

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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.

Tracking performance across complex assessments used to involve a lot of messy work.

Before this tool, grading was painful. You'd get raw answer sheets and have to manually write scripts or build massive spreadsheets just to compare arrays, calculate weighted averages, and isolate weaknesses by category. It was tedious copy-pasting between tabs, adjusting formulas until they broke, and spending half a day just trying to figure out the average time per question.

Now you send the data into this MCP. The system handles all that complexity—the weights, the categories, the timing—and spits out one clean report with every metric ready for use. You get instant, actionable intelligence instead of spreadsheets full of errors.

The score_quiz tool lets you move from raw data to deep analysis.

You no longer have to write separate functions for calculating weighted averages or another one just to isolate category failures. The single call using the score_quiz tool handles all that logic internally, keeping your codebase clean and fast.

The difference is simple: you stop writing grading logic and start building features with perfect data.

What your AI can actually do with this

Grading assessments shouldn't require writing complex scripts just to compare arrays or calculate weighted averages. This MCP handles the entire grading pipeline using a hyper-optimized engine. You simply feed it the answer key, the user responses, and optionally the total time taken. The tool doesn't just give a final score; it breaks down performance by subject category—like isolating whether a student struggles with 'Math' versus 'Science.' This granular view reveals exactly where knowledge gaps exist.

Because this MCP is hosted on Vinkius, you connect once from any compatible agent and get immediate access to advanced educational evaluation tools for your application.

The resulting data includes the max possible score calculation, percentage accuracy, and even derives a critical metric: the average time spent per question. This saves developers hours of writing complex scoring logic.

Built · Hosted · Managed by Vinkius Quiz Scorer MCP - Weighted Grading & Metrics
Server ID 019e38df-b574-7096-b24a-62fb03cc3b55
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How does score_quiz handle different point weights? +

score_quiz supports dynamic weighting. You define the weight for each question, and the tool accurately calculates the total possible score and the student's weighted achievement against it.

Can I get category weakness reports using score_quiz? +

Yes, the MCP generates granular analytics. It breaks down performance by predefined categories (like 'Math' or 'Science'), showing exactly which subject area needs improvement.

Does score_quiz track how long students take to finish? +

The tool accepts total time in seconds. From that figure, it automatically derives the average time spent per question, giving you a valuable performance metric for analysis.

Is score_quiz only for multiple choice quizzes? +

No. While it handles multiple-choice structure, its power is in its ability to analyze structured data across different academic categories and complex point systems.

How does the `score_quiz` handle malformed or incomplete input data? +

The tool validates all required strings before processing. If you pass a JSON array that's improperly structured or missing keys, it throws an explicit error detailing exactly which string failed validation and why.

Is `score_quiz` limited to objective questions, or can I grade subjective answers? +

The tool is designed for deterministic scoring based on comparison to a weighted key. For open-ended text entries, you must map the expected concepts and keywords into your answer keys; it does not run natural language checks.

How fast is `score_quiz`? Is it suitable for live, real-time grading? +

It runs with microsecond speed. Because it leverages a pure JS runtime and avoids massive external dependencies, the latency is minimal, making it ideal for integrating into live assessment workflows.

Does `score_quiz` require specific EdTech libraries or complex setup beyond its environment? +

No. The MCP guarantees a zero-dependency architecture using pure JS runtime execution. This means you don't need to worry about installing large, external EdTech NPM packages.

Why should I use an MCP instead of asking the AI to grade it? +

LLMs hallucinate math. If you give an LLM 50 questions, it will often miscount the correct answers, fail to apply fractional weights, or hallucinate the final percentage. This MCP uses deterministic V8 loops, guaranteeing 100% mathematical accuracy.

How does the weighting system work? +

In your answerKey JSON array, you can add a weight parameter (e.g., weight: 2.5). The engine automatically tallies the maxPossibleScore and evaluates the user's earned points against it, rather than just doing a flat 1-point-per-question calculation.

Does it track which questions the user got wrong? +

Yes. The output payload includes an array called incorrectQuestionIds, which isolates the exact IDs the user failed, allowing your AI to instantly provide targeted tutoring on those specific topics.

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Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
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