Forgetting Curve Calculator MCP for AI. Stop forgetting what you just learned.
Works with every AI agent you already use
…and any MCP-compatible client








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Forgetting Curve Calculator predicts memory decay using Ebbinghaus principles, telling you exactly when and how much you need to study to keep what you learn.
Stop guessing whether your review schedule is too loose or too intense. This MCP tracks how knowledge degrades over time, identifying critical drop-off points so you can build a precise, long-term learning plan.
What your AI can do
Estimate maintenance effort
Calculates how many future review sessions are needed to keep a topic stable long-term.
Get current retention
Gives you the exact percentage of memory strength for a given subject right now.
Predict decay thresholds
Predicts specific dates when your retention will fall below critical levels like 70% or 50%.
Instantly calculate the percentage of information you still remember after a period of time.
Predict the specific dates and thresholds (70%, 50%, etc.) when your knowledge is likely to drop below a functional level.
Estimate the total number of future study sessions required to maintain a desired level of retention over years.
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Forgetting Curve Calculator: 3 Tools
These tools allow your agent to calculate current retention rates, forecast knowledge decay points, and plan long-term maintenance effort.
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 Forgetting Curve Calculator on VinkiusEstimate Maintenance Effort
Calculates how many future review sessions are needed to keep a topic stable long-term.
Get Current Retention
Gives you the exact percentage of memory strength for a given subject right now.
Predict Decay Thresholds
Predicts specific dates when your retention will fall below critical levels like 70%...
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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 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manual Review Planning Is Hard
Right now, planning study time feels like a mess of sticky notes and vague feelings. You keep track of when you last looked at something, but calculating the next best review date—the one that hits the sweet spot between forgetting it completely and wasting time re-reading what you know—is impossible to do by hand.
With this MCP, your agent calculates those ideal intervals for you. Instead of guessing, you get a precise schedule telling you exactly when your knowledge is weakest and how much effort you need to put in to keep it stable.
Predict Decay Thresholds
You don't have to manually check multiple dates and percentages on a graph. The MCP handles the complex math, giving you clear warnings about when your knowledge will dip below 70%, 50%, or 20% retention.
This means you shift from reactive studying (cramming for immediate tests) to proactive planning—you address the problem before it becomes a failure.
What your AI can actually do with this
Learning something new—whether it's a complex programming concept or a historical timeline—is only half the battle. The real work is keeping it in your head later. This MCP helps map that decay process using established memory principles. Instead of just feeling like you 'forgot' something, you get data on when and why.
It calculates your current knowledge strength, predicts key failure points (like dropping below 70% retention), and even estimates the total review effort required for lasting mastery. You can connect this to Vinkius and let your AI agent automate scheduling based on actual memory decay rates, turning guesswork into predictable study cycles.
019ee7e3-5df6-71a2-9ac9-4a410c1b94a6 Here's how it actually works
The bottom line is, it replaces gut feelings about memory with hard data points for better study planning.
Provide your AI client with basic data: when you studied, how many times you reviewed it, and the time gaps between those reviews.
The MCP processes this input against the Ebbinghaus model to calculate decay rates and predict future performance dips.
You receive clear metrics showing your current retention percentage or a schedule detailing required review sessions.
Who is this actually for?
Anyone who has to retain information over time. Think medical students prepping for board exams, corporate trainers building curricula, or software engineers mastering new frameworks. If you're tired of cramming and want a predictable study plan, this is for you.
Needs to track complex physiological knowledge retention across multiple subjects to pass board exams.
Designs curricula and training modules that require long-term behavioral change or procedural recall, tracking required follow-up training cycles.
Learns new industry standards or programming languages on the fly and needs to structure their learning path to ensure retention months after initial training.
What Changes When You Connect
Instead of relying on 'good enough' study habits, get_current_retention gives you a hard number for your memory strength. You know exactly where you stand with zero guesswork.
You stop wasting time reviewing things that are already solid. By predicting when decay happens, this MCP lets you focus review energy only on the topics that genuinely need it.
The predict_decay_thresholds tool warns you weeks ahead of a critical drop-off point (like 70%). You can schedule preemptive reviews before forgetting actually happens.
It moves beyond simple flashcard systems. Using estimate_maintenance_effort, you build a full, realistic timeline for mastery that lasts years, not just until the next test.
Your agent doesn't just remind you to study; it calculates how much time you need to spend studying and when.
See it in action
Preparing for a Certification Exam
A developer studied advanced cloud security modules. They ask their agent, 'Based on my study dates and reviews, what's my current retention?' The tool uses get_current_retention to confirm they are at 80%, which is enough time to start planning the next phase of review.
Maintaining Core Industry Knowledge
A consultant mastered a complex financial regulation months ago. They use the MCP to predict when their knowledge will drop below 70% using predict_decay_thresholds, scheduling mandatory refresher sessions three weeks before the predicted dip.
Designing a Training Program
A corporate trainer needs to ensure employees retain new safety protocols. They use the MCP and estimate_maintenance_effort to determine that quarterly reviews are necessary for long-term stability, budgeting time into the annual training calendar.
Studying a New Language
A student learns vocabulary in bursts of 10 days. They feed this data to the MCP and use predict_decay_thresholds to see that they will drop below 50% retention in about two weeks, prompting them to schedule an immediate review cycle.
The honest tradeoffs
Studying everything equally
Spending equal time reviewing Topic A and Topic B, even though the data shows Topic A is solid (95% retention) but Topic B is failing fast.
First, use get_current_retention on both topics. Then, let your agent prioritize focus on Topic B because it requires immediate attention to prevent decay.
Waiting until the last minute
Cramming for a test right before it's due date, only to realize that even two weeks of intense study won't build lasting memory.
Use predict_decay_thresholds early in your learning process. It flags when you need to start reviewing before the knowledge dips below 70%.
Assuming continuous effort
Thinking that simply studying for a few months is enough, without planning for decay over years.
Use estimate_maintenance_effort to get a realistic count of future review sessions needed. This tool forces you to plan for long-term stability.
When It Fits, When It Doesn't
Use this MCP if your primary problem is memory retention and decay—you need to know when you'll forget something, not just what you forgot today. It’s a predictive scheduling engine.
Don't use it if you are trying to manage task flow, track deadlines for projects, or handle resource allocation (use a general project management tool instead). If your problem is 'I need to remember X,' this MCP works. If your problem is 'I need to do Y by Friday,' you're looking at the wrong type of tool.
Questions you might have
How does get_current_retention work? +
It calculates your memory strength as a percentage based on the study history you provide. This gives you an immediate, data-backed measure of how much you actually retained.
When should I use predict_decay_thresholds? +
Use this when you need to know your long-term risk profile. It predicts specific dates when critical retention levels are expected to drop, prompting you to schedule reviews early enough to prevent failure.
Is estimate_maintenance_effort just for estimating time? +
No. It estimates the number of future review sessions needed across a timeline to keep your knowledge stable and maintain a high level of retention over months or years.
Does this MCP work with my existing learning tools? +
Yes, it acts as an analytical layer. You feed it the data from your notes and study sessions, letting the MCP calculate the decay curve without needing to change how you gather information.
What specific data points must I provide when running `get_current_retention`? +
You need three key pieces of information: the initial study date, the total number of revisions performed, and the average time interval between those reviews. Providing this historical context gives the MCP enough data to calculate your current memory strength accurately.
What happens if I use `predict_decay_thresholds` with impossible dates? +
The tool requires a logically sequenced set of inputs, including valid starting and interval dates. If the MCP detects an invalid date range or conflicting data points, it returns an error code telling you exactly which input needs correction.
Are there any rate limits when running all three prediction tools? +
Vinkius manages high volume, but if you send a large number of requests in quick succession, the system may temporarily throttle you. We recommend batching your calls to keep your learning scheduling process smooth and reliable.
Does this MCP store my personalized retention data for later use? +
No, Vinkius processes your requests through your AI client session but doesn't permanently store your personal memory scores or study history. Your learning data remains private to your connected agent.
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