# Forgetting Curve Calculator MCP > 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. ## Overview - **Category:** productivity - **Price:** Free - **Tags:** ebbinghaus, learning, memory, retention, study-planner, spaced-repetition ## Description 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. ## Tools ### 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%. ## Prompt Examples **Prompt:** ``` I studied biology on 2024-05-01. I have done 2 reviews with a 7-day interval. What is my current retention? ``` **Response:** ``` Your current memory retention for biology is 85%. ``` **Prompt:** ``` Based on my study date of 2024-06-10, 3 revisions, and a 5-day interval, when will my retention drop below 70%? ``` **Response:** ``` Your retention is predicted to drop below the 70% threshold in approximately 4 days. ``` **Prompt:** ``` I want to maintain a 90% retention for my history topic. I currently have 75% retention with a 10-day interval. How many more reviews do I need? ``` **Response:** ``` You will need approximately 3 more review sessions to reach and maintain your target of 90% retention. ``` ## Capabilities ### Determine current knowledge strength Instantly calculate the percentage of information you still remember after a period of time. ### Forecast memory failure points Predict the specific dates and thresholds (70%, 50%, etc.) when your knowledge is likely to drop below a functional level. ### Plan for long-term review sessions Estimate the total number of future study sessions required to maintain a desired level of retention over years. ## Use Cases ### 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. ## Benefits - 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. ## How It Works The bottom line is, it replaces gut feelings about memory with hard data points for better study planning. 1. Provide your AI client with basic data: when you studied, how many times you reviewed it, and the time gaps between those reviews. 2. The MCP processes this input against the Ebbinghaus model to calculate decay rates and predict future performance dips. 3. You receive clear metrics showing your current retention percentage or a schedule detailing required review sessions. ## Frequently Asked Questions **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.