How to Build Your Learning Optimizer Agent

• Claude Desktop
• Cursor
• Microsoft Copilot Studio
• AWS Bedrock
• Google Gemini Enterprise
• Other compatible MCP clients and agent-building platforms

The available connection methods differ by platform. Check the client's current MCP support and configuration requirements before proceeding.

The goal is to give your agent access to the Learning Optimizer's specialized tools, so it can request review schedules and retention analysis instead of relying only on general-purpose language-model responses.

Step 2: Prepare Your Learning Data

The Learning Optimizer works with information about learners, learning items, and review events. The data you need depends on the operation you want to perform.

Step 3: Choose How Your Agent Will Access the Tools

There are two key parts to the setup: supplying the learning data and connecting the analysis tools.

Your application or data pipeline provides the review events, item identifiers, and other required inputs. The MCP exposes the Learning Optimizer tools to a compatible client.

Steps to Enable MCP on AI Platforms

Imagine you operate a test-preparation app. Learners complete practice questions every day, but your platform still needs a systematic way to decide which questions should return for revision and when.

Here's how the Learning Optimizer can support that workflow.

1. Schedule the Next Review for a Learning Item

   A learner reviews a question or flashcard. Your application supplies the learner identifier, item identifier, and latest review result.

   The schedule_review tool can use this information to return a recommended next review time and an estimated probability that the learner will recall the item.

   This helps your application move beyond a fixed schedule that treats every learner and every item the same way.

   Example prompt: “Based on this learner's latest result for the selected flashcard, schedule its next review and explain the recommendation.”

2. Generate Today's Review Queue

   Learners often need help deciding what to study next. The due_reviews tool can retrieve the items due for a learner on a specified date and return a priority-ordered review queue.

   Your application can use this information to present a focused daily revision list rather than asking learners to choose from the entire content library.

   Example prompt: “Show this learner's reviews due today, in priority order, and summarize what they should work on first.”

3. Estimate Future Recall

   Sometimes a learning platform needs to understand how likely a learner is to remember an item at a future point—for example, before an upcoming practice test.

   The predict_retention tool estimates the probability of recall for a selected learner-item pair at a specified future date. This can help the platform identify items that may deserve additional revision.

   The result is a model-based estimate, not a guarantee of the learner's future performance.

   Example prompt: “Estimate this learner's recall probability for the selected item on the date of their next practice test.”

4. Build a Study Plan Around an Exam Date

   A learner preparing for a competitive exam may have hundreds of questions or concepts to revisit before the test. A generic schedule may not account for the review history of individual items.

   The optimize_study_plan tool can use learner information, an exam date, and an item bank to create a structured review schedule designed to support retention by the target date.

   This gives test-preparation platforms a foundation for exam-oriented revision planning rather than relying entirely on manual scheduling.

   Example prompt: “Create a review plan for this learner using the available item bank and upcoming exam date. Organize the schedule to support retention by exam day.”

5. Understand How Memory Changes Over Time

   A learning item that was easy to recall yesterday may become harder to remember after a longer gap. The forgetting_curve tool can return a fitted decay curve and associated model parameters for a learner-item pair.

   This provides a way to inspect how the selected model represents memory change over time. It can also help developers and learning teams understand the information that informs scheduling decisions.

   Example prompt: “Show the modeled forgetting curve for this learner and item, and explain what it suggests about the review schedule.”

6. Explain Why a Review Was Scheduled

   Personalized scheduling is easier to trust when the reason behind a recommendation can be explained.

   The explain_schedule tool is designed to explain why a particular interval was chosen, including the model and confidence information available. This can help a learning product provide more context about review recommendations instead of showing a date without explanation.

   Example prompt: “Explain why the next review for this item is scheduled at this interval, and describe the model's confidence and limitations.”

7. Adapt Scheduling as Review History Accumulates

   A learning product may start with limited historical data. A baseline method such as SM-2 can provide a practical starting point, while more data-driven approaches such as Half-Life Regression and FSRS can use relevant review history for more personalized modeling.

   The initial model choice, available data, and quality of implementation determine what can be personalized. More review events do not automatically guarantee better predictions; performance should be evaluated against actual learner outcomes.

   Example prompt: “Review the available learning history and explain which scheduling approach is appropriate for this stage of the product, given the data available.”

That's the Shift

From fixed revision intervals and manual study planning to a more structured, data-informed review workflow.

The Learning Optimizer MCP gives compatible applications access to specialized tools for review scheduling, daily review queues, retention estimates, forgetting-curve modeling, and exam-oriented study planning.

The result is a foundation for more personalized learning experiences—without requiring every EdTech team to build its own spaced-repetition engine from scratch.