SM2 Spaced Repetition MCP for AI. Stop guessing when you need to review your flashcards.
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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.
It determines the mathematically correct next review date based on user performance.
The MCP adjusts a card's inherent difficulty factor (E-factor) after every successful or failed review.
You can update dozens of cards and their due dates with one single command.
It updates a card's status (e.g., 'due,' 'mastered') based on the input scores.
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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.
019ee7e4-a8e5-708d-affa-ca0af571ece0 Here's how it actually works
The bottom line is you get a completely accurate, mathematically driven review schedule without doing any manual math.
You instruct your agent to run the review cycle, providing a list of card IDs and their corresponding performance scores.
The MCP processes this data through the SM-2 algorithm, calculating new difficulty factors and optimal future intervals for each card in the batch.
Your agent receives confirmation with all updated due dates, ensuring your study schedule is accurate.
Who is this actually for?
This MCP serves anyone who relies on structured memory retention: medical students, language learners, or compliance trainers. If your job requires remembering complex facts over long periods, this is for you.
Uses it to manage vast amounts of anatomical and pharmacological data, ensuring they don't forget key drug interactions weeks before an exam.
Manages vocabulary recall for a class roster, assigning targeted review batches based on individual student performance scores.
Keeps track of compliance facts or system procedures that must be recalled accurately months after initial training.
What Changes When You Connect
You get precise scheduling. Instead of manually estimating 'maybe next week,' the evaluate_review_batch tool calculates the exact optimal date using proven algorithms.
It handles volume efficiently. If you have a stack of 50 cards, you process them all in one go through batch evaluation, instead of clicking on 50 individual records.
The difficulty factor is always correct. Every score (from 0 to 5) automatically adjusts the card's underlying difficulty metric, keeping your study model accurate over time.
Predictable memory retention. You trust that the schedule isn't just a guess; it’s based on mathematical principles proven effective in cognitive science.
Time savings are massive. It removes the need to track dates across multiple spreadsheets and manual calendar entries.
See it in action
Cramming for a certification exam
A paralegal needs to review 200 key legal definitions before an exam. Instead of logging into the database and manually updating dates, they feed all 200 IDs and their recall scores to evaluate_review_batch. The system instantly updates the due dates for everything, prioritizing what's weakest.
Teaching a new language
A tutor needs to check on three students who are learning Spanish vocabulary. They run their scores through evaluate_review_batch so that each student gets targeted practice—some get cards due tomorrow, others get them in two weeks.
Managing medical knowledge
A resident doctor finishes a rotation and has hundreds of drug facts to review. Running evaluate_review_batch allows the agent to process all performance data at once, ensuring no critical fact falls off the radar.
The honest tradeoffs
Manual Date Adjustment
The user reviews 10 cards and manually changes the due date in a spreadsheet for each one, leading to inconsistent data entry across records.
Use evaluate_review_batch. You provide the scores once, and the MCP handles all the complex math required to set accurate future dates automatically.
Ignoring Performance Scores
The user only reviews cards they 'feel' bad at, skipping those that are due but were mastered quickly, which skews their long-term retention rate.
Always pass all available scores to evaluate_review_batch. It uses the full range of performance data (0 through 5) to keep the algorithm accurate.
Running reviews in chunks
The user runs review batches over several days, resulting in fragmented updates that don't reflect a single, consistent learning session.
Consolidate your data. Use evaluate_review_batch to process all related card IDs and scores in one comprehensive call.
When It Fits, When It Doesn't
Use this MCP if your study material requires algorithmic scheduling based on performance history, like language vocab or scientific facts. You need a system that calculates optimal intervals—that's the core function. Don't use it if you just need to look up a single definition; that’s better handled by a simple read-only data retrieval tool. Also, don't use it for unstructured material; this MCP is strictly for card-based review systems and score evaluation using evaluate_review_batch. If your goal is merely tracking historical view counts, you need a different type of utility.
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.
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