A
licenseNot graded
qualityC
maintenanceEnables MCP-compatible AI clients to tutor students through a full learning workflow, including knowledge retrieval, assessments, mastery tracking, and personalized next-action recommendations.
MIT
leap-mcp
by Vinger-lee
Related Servers
Alternatives to leap-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI clients to create spaced-repetition flashcards from material being learned, quiz users aloud, grade answers, and schedule reviews using the FSRS algorithm through a self-hosted MCP server.1AGPL 3.0
- AlicenseAqualityBmaintenanceAn agent-native teaching OS that gives AI assistants a persistent classroom with courses, lessons, flashcards, and graded exercises via MCP tools.503AGPL 3.0
- AlicenseNot gradedqualityBmaintenanceAn authenticated MCP server that lets agents create, edit, search, paginate, and delete flashcards, organize decks, and record FSRS-scheduled reviews with optimistic concurrency and idempotent updates.11 npmMIT
- AlicenseBqualityDmaintenanceAn MCP server for tracking student learning via a knowledge graph, with spaced repetition scheduling and intelligent queries to identify gaps, misconceptions, and optimal next steps.71MIT
- AlicenseAqualityCmaintenanceAn MCP server that enforces a pedagogical workflow to teach topics step by step, using research, decomposition, and spaced retrieval practice.71MIT
TDQS
B3/5.0
Scored across 58 tools
Disambiguation3/5
Most tools target distinct resources and actions, but several pairs overlap: get_review_state and get_due_reviews both report due review items, and get_session_info and get_teaching_context both return session/teaching state. The descriptions help separate them, but with 58 tools an agent is still at risk of misselecting between closely grouped get/save/generate/submit operations.
Naming Consistency5/5
Tool names follow a highly consistent snake_case verb_noun pattern throughout: get_session_info, save_knowledge_nodes, submit_diagnostic, commit_assessment, advance_unit, record_reflection. Despite the large number of tools, the naming convention is uniform and predictable, making the surface easier to navigate.
Tool Count2/5
With 58 tools, the surface is far beyond the 3-15 range that typically indicates a well-scoped server. Many operations could be consolidated (e.g. get_review_state/get_due_reviews, save/get pairs), and the sheer count will burden the agent's tool-selection step even if each tool is individually useful.
Completeness5/5
The tool set covers the full learning lifecycle: session creation, goals, configuration, domain grounding, diagnostics, knowledge graph management, teaching, assessment, review scheduling, misconception tracking, transfer probes, artifacts, evidence, export, and final reporting. There are no obvious dead ends or missing core operations for the apparent domain.
Maintenance
ActivityMaintained
ResponsivenessNo issues