Vinkius
SM2 Spaced Repetition

SM2 Spaced Repetition MCP for AI. Stop guessing when you need to review your flashcards.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Works with every AI agent you already use

…and any MCP-compatible client

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Connect to your AI in seconds.

SM2 Spaced Repetition uses the proven SM-2 algorithm to manage your flashcard review schedule. It calculates optimal intervals and updates difficulty factors based on how well you know a card.

You can process entire batches of reviews in one go, making memory retention efficient for deep study.

Calculate Optimal Intervals

It determines the mathematically correct next review date based on user performance.

Update Difficulty Factors

The MCP adjusts a card's inherent difficulty factor (E-factor) after every successful or failed review.

Process Review Batches

You can update dozens of cards and their due dates with one single command.

Manage Card Statuses

It updates a card's status (e.g., 'due,' 'mastered') based on the input scores.

Included with Plan

Waiting for input…

AI Agent

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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 SM2 Spaced Repetition integration is available immediately — no restart needed.

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
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  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with SM2 Spaced Repetition, 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
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  • Every connection is secured and compliant automatically
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  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog every week
SM2 Spaced Repetition MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SM2 Spaced Repetition. 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.

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

The Struggle With Manual Study Scheduling

Today, keeping track of what you know and when you'll forget it is painful. You’re looking at spreadsheets, jumping between tabs, trying to remember if a card should be reviewed in seven days or fourteen. You copy data from one system into another just so you can manually adjust dates and calculate the next optimal review cycle.

With this MCP, that whole process disappears. You simply give your agent the list of cards and the scores for how well you know them. The system handles all the scheduling math automatically. You get a clean slate with perfectly updated due dates, ready for your next study session.

Using evaluate_review_batch

You no longer have to calculate interval shifts or adjust the difficulty factor by hand. The system takes the performance data you input and runs it through its core logic, updating all the complex math behind spaced repetition instantly.

What's different now is that your review cycle isn't based on guesswork; it’s based on a proven algorithm. Your agent gets back a complete list of updated cards, ready for immediate study.

What your AI can actually do with this

Managing what material you need to see next is tough. Most studying requires constant manual tracking: 'Was that 3 days? No, maybe 4? Should I count it as easy or good?' This MCP handles the math behind effective spaced repetition. It takes your raw performance data—like a score from 0 to 5—and runs it through the scientifically backed SM-2 algorithm.

The system instantly determines the optimal next review interval and adjusts the card's difficulty factor. When you connect this MCP via Vinkius, your agent processes multiple updates in a single call. You just feed the scores, and the MCP handles all the complex scheduling logic, giving you predictable, efficient memory retention without having to manually adjust dates or factors for every single card.

Built · Hosted · Managed by Vinkius SM2 Spaced Repetition - Algorithmic Review Scheduling
Server ID 019ee7e4-a8e5-708d-affa-ca0af571ece0
Vinkius Inspector
Compliance Grade B
Score 85/100
Vinkius Inspector Badge — Score 85/100

Questions you might have

How does the SM-2 algorithm work? +

It uses review quality scores (0-5) to adjust the interval between reviews and the Easiness Factor, helping to schedule reviews at the peak of your forgetting curve. Tools available: your_tool_name.

Can I process multiple cards at once? +

Yes, the evaluate_review_batch tool allows you to send a batch of card updates in a single request for maximum efficiency.

What are the supported card statuses? +

The system manages cards through three stages: New, Learning, and Mature.

If I give `evaluate_review_batch` scores that are out of range, what happens? +

The MCP validates your input immediately. If any score or ID fails the required range (e.g., outside 0-5), the tool tells you exactly which values are invalid before processing anything else.

What is the recommended size for a batch when using `evaluate_review_batch`? +

While there isn't a hard limit, keep batches under 50 cards. Processing large volumes in smaller chunks prevents timeouts and ensures reliable scheduling updates.

What specific data types must I pass to the MCP when running a review batch? +

You need card IDs and integer scores. The tool expects these values to be clean integers; non-integer inputs will cause the process to fail validation.

Can I set custom default parameters for new cards before calling `evaluate_review_batch`? +

Yes, you can override the standard initial settings. You specify your desired Easiness Factor or starting interval directly in the function call arguments.

If I need to calculate review intervals for a massive dataset, are there performance limitations? +

The MCP is designed for high throughput. For extremely large datasets, break them into manageable batches of 25-50 cards per call to maintain optimal processing speed.

Built & Managed by Vinkius 30s setup

We've already built the connector for SM2 Spaced Repetition. Just plug in your AI agents and start using Vinkius.

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This connector is live and waiting. You're up and running in seconds.

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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