Learning Optimizer
ai.saifsPublished by saifs.aiOfficial publisherv1.0.1Updated Oct 30, 2026
Schedule spaced reviews and exam study plans from each learner's forgetting curve.
The publisher's own server: Customer Intelligence & Segmentation
Overview
A remote spaced-repetition engine that lets an assistant schedule reviews, predict recall and build exam study plans from each learner's forgetting curve.
- What you can do
- Schedule and prioritize reviews: compute the next optimal review time for a learner-item and list today's due reviews in priority order.
- Predict and inspect memory: estimate recall probability at a future date and return the fitted forgetting curve and parameters for an item.
- Plan for exams: build a full schedule over an item bank so retention peaks on exam day.
- Explain results: show which model, evidence and confidence produced a given interval.
- When to use it
- Useful when an assistant helps with study planning, flashcards or exam preparation and needs quantitative review timing rather than generic advice. Suited to tutoring, language learning and any workflow where retention targets and exam dates drive scheduling.
- Requirements
- A remote Streamable HTTP endpoint; nothing to install locally. OAuth 2.1 sign-in with PKCE and dynamic client registration, so the MCP client opens a sign-in page on first use and must support OAuth-aware HTTP connections. Network access to the provider's endpoint.
Installation
In SourceWeft
- Open Learning Optimizer in the dashboard and add it to a workspace.
- Connect the required credentials. They are stored privately and never shown on public pages.
- Enable the server for the chats that should use its tools.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Use with an agent
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/ai.saifs%2Flearning-optimizer/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Other MCP clients
Add this to your client's mcpServers config. The client completes OAuth sign-in on first connect.
{
"mcpServers": {
"learning-optimizer": {
"type": "http",
"url": "https://learning-optimizer-mcp.saifs.ai/mcp"
}
}
}README
Learning Optimizer
A personalised spaced-repetition engine: it models each learner’s forgetting curve per item and schedules the fewest reviews that still hit a target retention. Built on Ebbinghaus, Leitner / SM-2 (Anki’s baseline), Half-Life Regression and FSRS. Also builds a full study plan that peaks retention by exam day.
Install
VS Code / GitHub Copilot
Add to .vscode/mcp.json (workspace) or your user mcp.json:
Then start the server from the MCP view and sign in when prompted.
Claude (claude.ai, Desktop)
Settings → Connectors → Add custom connector, and paste:
Claude Code
Any other MCP client
Point it at https://learning-optimizer-mcp.saifs.ai/mcp using the Streamable HTTP transport. The server returns 401 with a
WWW-Authenticate: Bearer … resource_metadata=… challenge, so OAuth-aware clients sign in automatically.
Tools
Example prompts
- “This student just missed item 142. When should they review it next, and what’s P(recall) then?”
- “What should this learner review today, in priority order?”
- “Exam is 28 days out. Build a study plan over this item bank so they peak on the day.”
Support
- Website: https://saifs.ai
- Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
- Privacy: https://saifs.ai/privacy_policy
Source: servers/learning-optimizer/README.md at commit f5c0037
Tools
0Version history
2- v1.0.1LatestOct 29, 2026
- v1.0.0Oct 22, 2026
Score
85% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 daysRequired25 pts
- Auth declaredRequired10 pts
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
Other Saifs integrations
Customer Intelligence & Segmentation
ai.saifsOfficial publisher
Remote MCP server that tiers a customer book into A/B/C/D segments from purchase history and builds a sales call list.
Dynamic Pricing & Overbooking
ai.saifsOfficial publisher
Remote MCP server that gives an assistant revenue-management math: recommended prices, protection levels, overbooking limits and booking-pace alerts.
Statistical Process Control & Anomaly Alerts
ai.saifsOfficial publisher
Remote MCP server that applies statistical process control charts to metric streams and alerts only on genuine out-of-control drift.
Payment Reconciliation & Matching
ai.saifsOfficial publisher
Remote MCP server that matches bank receipts, bank lines and GST 2B rows to open invoices and the ledger, with confidence scores and an exceptions queue.
Optimization Solver (LP / MIP Scheduling)
ai.saifsOfficial publisher
Remote MCP server that solves linear and mixed-integer optimization problems, returning optimal plans, shadow prices and infeasibility diagnostics.