Gauntlet-Incept MCP

# Gauntlet Incept Project: Implementation Checklist This document outlines the detailed steps for implementing the Gauntlet Incept Project, which aims to generate high-quality educational content tailored to students' knowledge levels and interests. ## Phase 1: Project Setup and Planning ### Environment Setup - [x] Initialize project repository structure - [x] Set up development environment - [x] Configure necessary dependencies - [x] Create documentation structure ### API Contract Definition - [x] Define request/response formats for all six endpoints - [x] `tagQuestion` endpoint specification - [x] `gradeQuestion` endpoint specification - [x] `generateQuestion` endpoint specification - [x] `tagArticle` endpoint specification - [x] `gradeArticle` endpoint specification - [x] `generateArticle` endpoint specification - [x] Document error handling strategies - [x] Establish performance expectations and SLAs - [x] Create API documentation template ### Data Access Layer - [x] Set up connection to Common Core Crawl (CCC) database - [x] Implement data retrieval functions for example content - [x] Create interfaces for the 1EdTech Extended QTI Implementation - [x] Develop data storage mechanisms for generated content ## Phase 2: Quality Control Framework ### Test Harness Development - [x] Design test harness architecture - [x] Implement `measureAccuracy` functionality - [x] Create storage for good and bad examples in QTI database - [x] Develop metadata tagging system for test examples - [x] Build reporting mechanism for precision, recall, and F1 scores ### Example Collection - [ ] Gather initial set of high-quality examples from CCC - [ ] Create bad examples through mutation testing - [ ] Tag all examples with complete metadata - [ ] Validate example tagging accuracy - [ ] Organize examples by subject, grade, standard, lesson, and difficulty ## Phase 3: Core Services Implementation - Question Generator ### Lesson Tagger - [x] Develop `tagQuestionWithLesson` functionality - [ ] Integrate with course definition data - [ ] Test accuracy against known examples - [ ] Implement error handling for out-of-scope questions - [ ] Document lesson tagging logic and limitations ### Question Grader - [x] Implement quality criteria evaluation for questions - [x] Develop scoring mechanism for each quality dimension - [x] Create feedback generation for failed questions - [ ] Test grader against known good and bad examples - [ ] Refine grading logic to achieve 99% precision ### Question Generator - [x] Implement internal question generation functionality - [x] Develop iteration mechanism to meet quality standards - [x] Create variant generation from example questions - [ ] Test generator output quality and diversity - [ ] Optimize for performance and resource usage ### Question Tagger - [x] Implement `tagQuestion` endpoint - [x] Develop logic for identifying subject, grade, standard, lesson, and difficulty - [ ] Test tagger accuracy against known examples - [x] Implement error handling for ambiguous or out-of-scope questions - [ ] Document tagging confidence thresholds ## Phase 4: Core Services Implementation - Article Generator ### Article Grader - [x] Implement quality criteria evaluation for articles - [x] Develop scoring mechanism for each quality dimension - [x] Create feedback generation for failed articles - [ ] Test grader against known good and bad examples - [ ] Refine grading logic to achieve 99% precision ### Article Generator - [x] Implement internal article generation functionality - [x] Develop worked example generation - [x] Create direct instruction style content - [ ] Test generator output quality and educational effectiveness - [ ] Optimize for grade-appropriate language and clarity ### Article Tagger - [x] Implement `tagArticle` endpoint - [x] Develop logic for identifying subject, grade, standard, and lesson - [ ] Test tagger accuracy against known examples - [x] Implement error handling for ambiguous or out-of-scope articles - [ ] Document tagging confidence thresholds ## Phase 5: Vertical Slice Implementation ### Pilot Lesson Selection - [ ] Analyze available course definitions - [ ] Select one lesson for initial implementation - [ ] Document selection criteria and rationale - [ ] Gather all relevant examples for the selected lesson - [ ] Define success criteria for the pilot implementation ### End-to-End Implementation - [ ] Integrate all components for the pilot lesson - [ ] Implement full workflow from tagging to generation - [ ] Create simple visualization for generated content - [ ] Test complete system with the pilot lesson - [ ] Document any integration issues and solutions ### Quality Assessment - [ ] Run comprehensive tests on the pilot implementation - [ ] Measure precision, recall, and F1 scores - [ ] Identify and address quality gaps - [ ] Document quality metrics and improvement strategies - [ ] Validate against educational effectiveness criteria ## Phase 6: Expansion and Refinement ### Difficulty Level Expansion - [ ] Extend pilot implementation to all difficulty levels - [ ] Test quality across difficulty spectrum - [ ] Refine generators for difficulty-appropriate content - [ ] Document difficulty level characteristics - [ ] Validate difficulty progression with educational experts ### Additional Lessons Implementation - [ ] Select next set of lessons for implementation - [ ] Adapt existing components for new lessons - [ ] Test cross-lesson consistency - [ ] Refine generators for lesson-specific content - [ ] Document lesson implementation patterns ### Subject Expansion - [ ] Extend implementation to additional subjects - [ ] Adapt generators for subject-specific requirements - [ ] Test quality across different subjects - [ ] Document subject-specific challenges and solutions - [ ] Validate educational effectiveness across subjects ## Phase 7: System Integration and Deployment ### API Finalization - [ ] Implement all six endpoints with full functionality - [ ] Create comprehensive API documentation - [ ] Develop usage examples and tutorials - [ ] Implement rate limiting and security measures - [ ] Test API performance under load ### Course Visualization - [ ] Develop course visualization interface - [ ] Implement QTI structure representation - [ ] Create navigation between lessons, articles, and questions - [ ] Test visualization with complete courses - [ ] Document visualization features and limitations ### Deployment - [ ] Set up production environment - [ ] Implement monitoring and logging - [ ] Create backup and recovery procedures - [ ] Document deployment architecture - [ ] Develop scaling strategy for increased usage ## Phase 8: Validation and Documentation ### Educational Validation - [ ] Conduct review with educational experts - [ ] Validate content against educational standards - [ ] Test with target student demographics - [ ] Gather feedback on educational effectiveness - [ ] Document validation results and improvements ### System Documentation - [ ] Create comprehensive system documentation - [ ] Document architecture and design decisions - [ ] Create user guides for API consumers - [ ] Document known limitations and future work - [ ] Prepare final project report ### Knowledge Transfer - [ ] Conduct training sessions for stakeholders - [ ] Create onboarding materials for new team members - [ ] Document maintenance procedures - [ ] Create troubleshooting guides - [ ] Establish support channels and procedures ## Success Criteria The project will be considered successful when: 1. All six API endpoints are fully implemented and documented 2. The quality control system achieves 99% precision 3. At least one complete course is generated and visualized 4. The system can generate content for multiple subjects, grade levels, and difficulty levels 5. Generated content meets all quality criteria defined in the project requirements 6. The system is deployed and accessible to stakeholders ## Progress Tracking This checklist will be updated regularly to track progress. Items will be marked as completed when they meet the defined success criteria for each task. Last Updated: [Current Date]