MCP Standards

# Implementation Roadmap - Memory Architecture v2.0 **Version**: 2.0.0 **Status**: Design Phase **Last Updated**: 2025-10-20 **Timeline**: 4 weeks (phased approach) --- ## Executive Summary Phased implementation plan for the comprehensive memory architecture integrating SQLite, AgentDB, hooks system, skills auto-generation, and CLAUDE.md management. Each phase builds on the previous, with clear deliverables and success criteria. --- ## Phase 1: Foundation & Storage (Week 1) ### Objectives - Implement dual-layer storage (SQLite + AgentDB) - Create migration utilities from existing ReasoningBank - Setup basic hooks infrastructure - Validate core architecture ### Deliverables #### 1.1 Dual-Layer Storage Implementation **Files**: - `src/mcp_standards/storage/dual_layer.py` - `src/mcp_standards/storage/migration.py` - `src/mcp_standards/storage/sync.py` **Tasks**: - [ ] Extend SQLite schema with new tables (embeddings, patterns, etc.) - [ ] Integrate AgentDB adapter with configuration - [ ] Implement reference ID synchronization - [ ] Create migration script from existing ReasoningBank - [ ] Add data consistency validation **Success Criteria**: - [ ] All new database tables created and indexed - [ ] AgentDB connected with HNSW indexing enabled - [ ] Existing episodes migrated with embeddings computed - [ ] Reference ID system working bidirectionally - [ ] 100% data consistency between SQLite and AgentDB #### 1.2 Basic Hooks Infrastructure **Files**: - `src/mcp_standards/hooks/capture_hook.py` (enhance existing) - `src/mcp_standards/hooks/event_router.py` (new) - `~/.claude/hooks.json` (configuration) **Tasks**: - [ ] Enhance capture_hook.py with event routing - [ ] Implement PreToolUse, PostToolUse, SessionEnd handlers - [ ] Add error handling and resilience (always exit 0) - [ ] Create hooks.json configuration template - [ ] Setup async processing for non-blocking execution **Success Criteria**: - [ ] Hooks trigger on tool execution in Claude Code - [ ] Events captured with <5ms overhead - [ ] Error handling prevents any failures - [ ] Hooks can be enabled/disabled via configuration - [ ] Basic logging functional #### 1.3 Migration & Data Validation **Files**: - `scripts/migrate_to_v2.py` - `tests/test_migration.py` **Tasks**: - [ ] Create comprehensive migration script - [ ] Compute embeddings for all existing episodes - [ ] Validate data integrity post-migration - [ ] Create rollback mechanism - [ ] Document migration process **Success Criteria**: - [ ] All existing data migrated successfully - [ ] Zero data loss during migration - [ ] Embeddings computed for 100% of episodes - [ ] Rollback tested and functional - [ ] Migration completes in <5 minutes for 10k episodes ### Testing ```bash # Unit tests pytest tests/test_dual_layer_storage.py pytest tests/test_migration.py pytest tests/test_hooks_basic.py # Integration tests pytest tests/integration/test_sqlite_agentdb_sync.py # Performance benchmarks python scripts/benchmark_storage.py ``` ### Week 1 Deliverables Checklist - [ ] Dual-layer storage operational - [ ] Migration from v1 to v2 successful - [ ] Basic hooks capturing tool executions - [ ] All tests passing - [ ] Performance within targets (<5ms hook overhead) - [ ] Documentation updated --- ## Phase 2: Intelligence & Learning (Week 2) ### Objectives - Implement 5-layer pattern detection - Add confidence-based learning system - Create pattern frequency tracking - Build temporal knowledge graph ### Deliverables #### 2.1 Pattern Detection System **Files**: - `src/mcp_standards/hooks/pattern_extractor.py` (enhance existing) - `src/mcp_standards/intelligence/pattern_learner.py` (new) - `src/mcp_standards/intelligence/semantic_clustering.py` (new) **Tasks**: - [ ] Implement Layer 1: Explicit corrections detection - [ ] Implement Layer 2: Implicit rejections detection - [ ] Implement Layer 3: Rule violations detection - [ ] Implement Layer 4: Behavioral patterns detection - [ ] Implement Layer 5: Semantic clustering via AgentDB - [ ] Create unified pattern learning pipeline **Success Criteria**: - [ ] Layer 1 detects 95%+ explicit corrections - [ ] Layer 2 detects 85%+ implicit rejections (2-min window) - [ ] Layer 3 detects 90%+ rule violations - [ ] Layer 4 detects repeated sequences (5+ occurrences) - [ ] Layer 5 clusters similar patterns via embeddings - [ ] Unified confidence scoring working #### 2.2 Confidence-Based Learning **Files**: - `src/mcp_standards/intelligence/confidence_scorer.py` (new) - `src/mcp_standards/intelligence/preference_manager.py` (new) **Tasks**: - [ ] Create multi-factor confidence scoring - [ ] Implement automatic preference promotion (3+ occurrences, 90%+ confidence) - [ ] Add preference lifecycle management - [ ] Build preference evolution tracking - [ ] Create preference validation system **Success Criteria**: - [ ] Confidence scores accurate (validated against test cases) - [ ] Auto-promotion triggers at 3 occurrences with 90%+ confidence - [ ] Preference lifecycle tracked in temporal graph - [ ] No false positives in auto-promotion - [ ] Preferences can be manually overridden #### 2.3 Temporal Knowledge Graph **Files**: - `src/mcp_standards/intelligence/temporal_graph.py` (enhance existing) **Tasks**: - [ ] Implement preference version tracking - [ ] Add change reason logging - [ ] Create preference evolution queries - [ ] Build preference deprecation system - [ ] Add knowledge graph visualization **Success Criteria**: - [ ] All preference changes tracked with timestamps - [ ] Change reasons captured - [ ] Can query preference history - [ ] Deprecated preferences handled gracefully - [ ] Evolution visible in MCP tools #### 2.4 Enhanced MCP Tools **Files**: - `src/mcp_standards/server.py` (enhance) **Tasks**: - [ ] Add `learn_preference` tool - [ ] Add `suggest_claudemd_update` tool - [ ] Add `query_agent_performance` tool - [ ] Add `validate_spec` tool - [ ] Add `check_quality_gates` tool **Success Criteria**: - [ ] All new tools functional in Claude Desktop/Code - [ ] Tools return structured data - [ ] Error handling comprehensive - [ ] Performance <100ms per tool call - [ ] Documentation in docstrings ### Testing ```bash # Unit tests pytest tests/test_pattern_detection.py pytest tests/test_confidence_scoring.py pytest tests/test_temporal_graph.py pytest tests/test_enhanced_mcp_tools.py # Integration tests pytest tests/integration/test_learning_pipeline.py # Accuracy validation python scripts/validate_pattern_detection.py ``` ### Week 2 Deliverables Checklist - [ ] 5-layer pattern detection operational - [ ] Confidence-based learning working - [ ] Temporal knowledge graph tracking changes - [ ] Enhanced MCP tools available - [ ] All tests passing with 80%+ coverage - [ ] Pattern detection accuracy >90% --- ## Phase 3: Auto-Generation & Updates (Week 3) ### Objectives - Implement skills auto-generation - Create CLAUDE.md smart merging - Build cross-tool config sync (optional) - Add real-time notifications ### Deliverables #### 3.1 Skills Auto-Generation **Files**: - `src/mcp_standards/skills/generator.py` (new) - `src/mcp_standards/skills/templates/` (new) - `src/mcp_standards/skills/validator.py` (new) **Tasks**: - [ ] Create skill generation pipeline - [ ] Build template rendering system (Jinja2) - [ ] Implement skill file writer - [ ] Add skill validation (YAML frontmatter, markdown) - [ ] Create INDEX.json management - [ ] Setup skill evolution/strengthening **Success Criteria**: - [ ] Skills auto-generated from 90%+ confident patterns - [ ] Generated skills follow standard template - [ ] YAML frontmatter valid - [ ] Skills discoverable by Claude Code - [ ] INDEX.json kept in sync - [ ] Skills strengthen with new evidence #### 3.2 CLAUDE.md Smart Merging **Files**: - `src/mcp_standards/intelligence/claudemd_manager.py` (enhance existing) - `src/mcp_standards/intelligence/claudemd_parser.py` (new) - `src/mcp_standards/intelligence/conflict_resolver.py` (new) **Tasks**: - [ ] Implement hierarchy detection (global → parent → project) - [ ] Create smart file selection logic - [ ] Build content parser for existing CLAUDE.md - [ ] Add intelligent conflict resolution - [ ] Implement token optimization - [ ] Create backup system - [ ] Add atomic write operations **Success Criteria**: - [ ] Hierarchy correctly detected - [ ] File selection logic accurate (validated with test cases) - [ ] Conflicts resolved without data loss - [ ] Token budget enforced (<10k tokens) - [ ] Backups created before updates - [ ] Updates atomic (all-or-nothing) #### 3.3 Cross-Tool Config Sync (Optional) **Files**: - `src/mcp_standards/crosstools/cursor_sync.py` (new) - `src/mcp_standards/crosstools/copilot_sync.py` (new) - `src/mcp_standards/crosstools/format_converters.py` (new) **Tasks**: - [ ] Implement Cursor .cursorrules format converter - [ ] Implement GitHub Copilot instructions converter - [ ] Implement Windsurf .windsurfrules converter - [ ] Add opt-in configuration - [ ] Create sync scheduler **Success Criteria**: - [ ] Format converters accurate - [ ] Opt-in system working - [ ] Sync creates timestamped backups - [ ] Changes reflected in other tools - [ ] No conflicts with manual edits #### 3.4 Real-Time Notifications **Files**: - `src/mcp_standards/notifications/notifier.py` (new) **Tasks**: - [ ] Implement MCP notification system - [ ] Add notification levels (info, important, critical) - [ ] Create notification templates - [ ] Add user preferences for notification filtering - [ ] Build notification history **Success Criteria**: - [ ] Notifications appear in Claude Desktop/Code - [ ] Levels respected (user can filter) - [ ] Templates clear and actionable - [ ] History accessible - [ ] No notification spam ### Testing ```bash # Unit tests pytest tests/test_skill_generation.py pytest tests/test_claudemd_merging.py pytest tests/test_crosstools_sync.py pytest tests/test_notifications.py # Integration tests pytest tests/integration/test_auto_generation_pipeline.py # Real-world validation python scripts/test_with_real_claude.py ``` ### Week 3 Deliverables Checklist - [ ] Skills auto-generated from learned patterns - [ ] CLAUDE.md updates working without conflicts - [ ] Cross-tool sync functional (opt-in) - [ ] Real-time notifications operational - [ ] All tests passing - [ ] Real-world validation with Claude Code successful --- ## Phase 4: Validation, Polish & Launch (Week 4) ### Objectives - Implement spec validation engine - Add agent performance tracking - Create quality gates system - Comprehensive testing and documentation - Production readiness ### Deliverables #### 4.1 Spec Validation Engine **Files**: - `src/mcp_standards/intelligence/validation_engine.py` (enhance existing) - `src/mcp_standards/intelligence/spec_parser.py` (new) - `src/mcp_standards/intelligence/gap_detector.py` (new) **Tasks**: - [ ] Implement spec capture and storage - [ ] Create deliverable comparison logic - [ ] Build gap detection algorithm - [ ] Add validation gate checking - [ ] Create validation reports **Success Criteria**: - [ ] Specs automatically captured from user messages - [ ] Deliverables accurately compared to specs - [ ] 90%+ gap detection accuracy - [ ] Validation reports clear and actionable - [ ] Quality gates prevent incomplete work #### 4.2 Agent Performance Tracking **Files**: - `src/mcp_standards/intelligence/agent_tracker.py` (enhance existing) - `src/mcp_standards/intelligence/performance_analyzer.py` (new) **Tasks**: - [ ] Implement agent execution tracking - [ ] Add success/failure rate calculation - [ ] Create correction frequency analysis - [ ] Build agent recommendation engine - [ ] Add performance dashboards (via MCP tools) **Success Criteria**: - [ ] All agent executions tracked - [ ] Success rates accurate - [ ] Recommendations based on historical data - [ ] Performance trends visible - [ ] Dashboards accessible via MCP tools #### 4.3 Quality Gates System **Files**: - `src/mcp_standards/intelligence/quality_gates.py` (new) **Tasks**: - [ ] Implement configurable quality gates - [ ] Add auto-check for common gates (tests, docs, build) - [ ] Create custom gate support - [ ] Build gate failure tracking - [ ] Add gate recommendations **Success Criteria**: - [ ] Quality gates configurable per project - [ ] Auto-checks work for common gates - [ ] Custom gates supported - [ ] Failures tracked and reported - [ ] Recommendations improve over time #### 4.4 Comprehensive Testing **Files**: - `tests/` (comprehensive coverage) - `tests/integration/` (end-to-end tests) - `tests/performance/` (benchmarks) **Tasks**: - [ ] Achieve 80%+ code coverage - [ ] Create end-to-end integration tests - [ ] Add performance benchmarks - [ ] Build real-world scenario tests - [ ] Create regression test suite **Success Criteria**: - [ ] Code coverage ≥80% - [ ] All integration tests passing - [ ] Performance benchmarks within targets - [ ] Real-world scenarios validated - [ ] No regressions detected #### 4.5 Documentation & Guides **Files**: - `docs/guides/INSTALLATION.md` - `docs/guides/QUICKSTART.md` - `docs/guides/ADVANCED_USAGE.md` - `docs/TROUBLESHOOTING.md` - `docs/API_REFERENCE.md` **Tasks**: - [ ] Create installation guide - [ ] Write quickstart tutorial - [ ] Document advanced features - [ ] Create troubleshooting guide - [ ] Generate API reference - [ ] Record video tutorial **Success Criteria**: - [ ] Installation guide tested by new user - [ ] Quickstart completes in <15 minutes - [ ] Advanced features well-documented - [ ] Troubleshooting guide comprehensive - [ ] API reference complete - [ ] Video tutorial published ### Testing ```bash # Full test suite pytest tests/ -v --cov=src/mcp_standards --cov-report=html # Integration tests pytest tests/integration/ -v # Performance benchmarks python scripts/benchmark_full_system.py # Real-world validation python scripts/validate_production_ready.py ``` ### Week 4 Deliverables Checklist - [ ] Spec validation preventing incomplete work - [ ] Agent performance driving recommendations - [ ] Quality gates enforced - [ ] 80%+ test coverage achieved - [ ] All documentation complete - [ ] Real-world validation successful - [ ] Production-ready release --- ## Success Metrics ### Technical Metrics | Metric | Target | Measurement | |--------|--------|-------------| | Hook Overhead | <5ms | `benchmark_hooks.py` | | Pattern Detection Accuracy | >90% | `validate_patterns.py` | | CLAUDE.md Token Budget | <10k | `token_counter.py` | | Skill Generation Time | <1s | `benchmark_skills.py` | | Test Coverage | ≥80% | `pytest --cov` | | Migration Time (10k episodes) | <5min | `benchmark_migration.py` | ### User Experience Metrics | Metric | Target | Measurement | |--------|--------|-------------| | Reduction in Repeated Instructions | 80% | User survey | | Skills Discovery Success | 100% | Claude Code integration test | | CLAUDE.md Conflicts | 0 | Integration tests | | Notification Spam | 0 | User feedback | | Setup Time | <15min | Quickstart guide completion | ### Learning System Metrics | Metric | Target | Measurement | |--------|--------|-------------| | Pattern Promotion Accuracy | >95% | Manual review | | Confidence Score Accuracy | >90% | Test cases validation | | Spec Validation Accuracy | >90% | Real-world testing | | Agent Recommendation Quality | >85% | User acceptance | --- ## Risk Mitigation ### High-Risk Areas 1. **Performance Impact** - Risk: Hooks slow down Claude Code - Mitigation: Async processing, 5s timeout, significance filtering - Monitoring: Hook execution time logging 2. **False Learning** - Risk: System learns incorrect patterns - Mitigation: Confidence thresholds, manual override, transparent logging - Monitoring: Pattern review dashboard 3. **Data Migration** - Risk: Data loss during v1→v2 migration - Mitigation: Backup-first, validation, rollback mechanism - Monitoring: Migration success rate tracking 4. **CLAUDE.md Conflicts** - Risk: Auto-updates conflict with manual edits - Mitigation: Smart merging, conflict detection, user review - Monitoring: Conflict occurrence tracking 5. **Token Budget Exceeded** - Risk: CLAUDE.md grows beyond 10k tokens - Mitigation: Optimization algorithm, /prime commands, warnings - Monitoring: Token usage dashboard --- ## Dependencies ### External Dependencies - Python ≥3.10 - `uv` package manager - Claude Desktop/Code (native hooks support) - AgentDB v1.0.7+ ### Internal Dependencies ``` Phase 1 (Foundation) → Phase 2 (Intelligence) ↓ Phase 3 (Auto-Generation) ↓ Phase 4 (Validation & Polish) ``` --- ## Team & Resources ### Required Expertise - Python backend development (storage, hooks, intelligence) - LLM integration (MCP protocol, embeddings) - Frontend integration (Claude Desktop/Code) - Testing & QA (comprehensive test coverage) - Documentation & DevRel (guides, tutorials) ### Time Commitment - Week 1: 40 hours (foundation) - Week 2: 40 hours (intelligence) - Week 3: 40 hours (auto-generation) - Week 4: 40 hours (validation & polish) **Total**: 160 hours (4 weeks) --- ## Launch Checklist ### Pre-Launch - [ ] All Phase 1-4 deliverables complete - [ ] All tests passing - [ ] Documentation reviewed - [ ] Real-world validation successful - [ ] Performance benchmarks within targets - [ ] Security review passed ### Launch - [ ] Tagged release (v2.0.0) - [ ] PyPI package published - [ ] Documentation deployed - [ ] Video tutorial published - [ ] Blog post published - [ ] Community announcement ### Post-Launch - [ ] Monitor error rates - [ ] Track user feedback - [ ] Respond to issues within 24h - [ ] Iterate based on usage data - [ ] Plan v2.1 improvements --- ## Communication Plan ### Week 1 Update **Audience**: Internal team, early adopters **Content**: Foundation complete, migration successful, hooks working **Channels**: GitHub discussions, Discord ### Week 2 Update **Audience**: Internal team, early adopters **Content**: Pattern learning operational, accuracy metrics, MCP tools available **Channels**: GitHub discussions, Discord, Twitter ### Week 3 Update **Audience**: Public (soft launch) **Content**: Auto-generation working, CLAUDE.md management, demo video **Channels**: GitHub release notes, Twitter, LinkedIn ### Week 4 Launch **Audience**: Public (full launch) **Content**: Production-ready v2.0.0, comprehensive guides, testimonials **Channels**: Product Hunt, Hacker News, Reddit, Twitter, LinkedIn, blog post --- ## Post-v2.0 Roadmap ### v2.1 (Month 2) - Multi-user learning (team knowledge graphs) - Advanced analytics dashboards - ML-based pattern prediction - Natural language preference input ### v2.2 (Month 3) - Federated learning across teams - Enterprise compliance features - Advanced workflow automation - Cross-organization knowledge sharing ### v3.0 (Month 6) - AI-driven context optimization - Predictive auto-suggestions - Multi-modal learning (code + docs + conversations) - Enterprise SaaS offering --- ## Conclusion This roadmap provides a structured, phased approach to implementing the comprehensive memory architecture. Each phase builds on the previous, with clear deliverables, success criteria, and risk mitigation strategies. **Key Principles**: - Incremental delivery - Continuous validation - User-centric design - Production quality from day 1 **Target**: Production-ready v2.0.0 in 4 weeks with 80%+ test coverage, <5ms performance impact, and comprehensive documentation. --- **Created by**: Memory Architecture Agent **Coordinated via**: Hive Mind Collective **Timeline**: 4 weeks (phased) **Status**: Ready for review and approval