babylon-mcp/ROADMAP.md

# Babylon MCP Server - Development Roadmap

## Vision
Build an MCP (Model Context Protocol) server that helps developers working with Babylon.js by providing intelligent documentation search and sandbox examples. The MCP server serves as a canonical, token-efficient source for Babylon.js framework information when using AI agents, while incorporating community feedback to continuously improve search relevance.

## Documentation Source
- **Repository**: https://github.com/BabylonJS/Documentation.git
- This is the authoritative source for all Babylon.js documentation

---

## Recent Progress (2025-01-23)

**Phase 1 Core Features - COMPLETED** ✅

Successfully implemented vector search with local embeddings:
- ✅ Installed and configured LanceDB + @xenova/transformers
- ✅ Created document parser with YAML frontmatter extraction
- ✅ Built indexer that processes 745 markdown files
- ✅ Generated vector embeddings using Xenova/all-MiniLM-L6-v2 (local, no API costs)
- ✅ Implemented `search_babylon_docs` MCP tool with semantic search
- ✅ Implemented `get_babylon_doc` MCP tool for document retrieval
- ✅ Added relevance scoring and snippet extraction
- ✅ Tested successfully with "Vector3" query

**Key Implementation Details:**
- Vector database: LanceDB stored in `./data/lancedb`
- Embedding model: Runs locally in Node.js via transformers.js
- Indexed fields: title, description, keywords, category, breadcrumbs, content, headings, code blocks
- Search features: Semantic similarity, category filtering, ranked results with snippets
- Scripts: `npm run index-docs` to rebuild index

---

## Phase 1: Core MCP Infrastructure & Documentation Indexing
**Goal**: Establish foundational MCP server with documentation search from the canonical GitHub source

### 1.1 MCP Server Setup
- [X] Install and configure MCP SDK (@modelcontextprotocol/sdk)
- [X] Implement MCP server with HTTP transport (SSE)
- [X] Define MCP server metadata and capabilities
- [X] Create basic server lifecycle management (startup, shutdown, error handling)

### 1.2 Documentation Repository Integration
- [X] Clone and set up local copy of BabylonJS/Documentation repository
- [X] Implement automated git pull mechanism for updates
- [X] Parse documentation file structure (markdown files, code examples)
- [X] Extract metadata from documentation files (titles, categories, versions)
- [I] Index Babylon.js source repository markdown files (Option 3 - Hybrid Approach, Phase 1)
  - [I] Add 144 markdown files from Babylon.js/Babylon.js repository
  - [I] Include: CHANGELOG.md, package READMEs, contributing guides
  - [ ] Phase 2: Evaluate TypeDoc integration for API reference
- [ ] Create documentation change detection system
- [ ] Research and fix Claude Code config file integration issue
  - CLI `/mcp http://localhost:4000/mcp` works
  - Config file `~/.claude/config.json` approach does not work
  - Need to investigate proper config file format for HTTP MCP servers

### 1.3 Search Index Implementation
- [X] Design indexing strategy for markdown documentation
- [X] Implement vector embeddings for semantic search (using @xenova/transformers with Xenova/all-MiniLM-L6-v2)
- [X] Create vector database with LanceDB
- [X] Index code examples separately from prose documentation
- [ ] Implement incremental index updates (only reindex changed files)

### 1.4 Basic Documentation Search Tool
- [X] Implement MCP tool: `search_babylon_docs`
  - Input: search query, optional filters (category, API section)
  - Output: ranked documentation results with context snippets and file paths
- [X] Return results in token-efficient format (concise snippets vs full content)
- [X] Add relevance scoring based on semantic similarity and keyword matching
- [ ] Implement result deduplication

### 1.5 Documentation Retrieval Tool
- [X] Implement MCP tool: `get_babylon_doc`
  - Input: specific documentation file path or topic identifier
  - Output: full documentation content optimized for AI consumption
- [X] Format content to minimize token usage while preserving clarity
- [X] Include related documentation links in results

---

## Phase 2: Sandbox Examples Integration
**Goal**: Enable discovery and search of Babylon.js Playground examples

### 2.1 Playground Data Source
- [ ] Research Babylon.js Playground structure and API
- [ ] Identify authoritative source for playground examples
- [ ] Determine if examples are in Documentation repo or need separate scraping
- [ ] Design data model for playground examples

### 2.2 Example Indexing
- [ ] Implement scraper/parser for playground examples
- [ ] Extract: title, description, code, tags, dependencies
- [ ] Index example code with semantic understanding
- [ ] Link examples to related documentation topics
- [ ] Store example metadata efficiently

### 2.3 Example Search Tool
- [ ] Implement MCP tool: `search_babylon_examples`
  - Input: search query, optional filters (features, complexity)
  - Output: ranked examples with descriptions and playground URLs
- [ ] Return code snippets in token-efficient format
- [ ] Add "similar examples" recommendations
- [ ] Include difficulty/complexity indicators

---

## Phase 3: Token Optimization & Caching
**Goal**: Minimize token usage for AI agents while maintaining quality

### 3.1 Response Optimization
- [ ] Implement smart content summarization for long documentation
- [ ] Create tiered response system (summary → detailed → full content)
- [ ] Remove redundant information from responses
- [ ] Optimize markdown formatting for AI consumption
- [ ] Add token count estimates to responses

### 3.2 Intelligent Caching
- [ ] Implement query result caching (Redis or in-memory)
- [ ] Cache frequently accessed documentation sections
- [ ] Add cache invalidation on documentation updates
- [ ] Track cache hit rates and optimize cache strategy
- [ ] Implement cache warming for popular queries

### 3.3 Context Management
- [ ] Implement MCP resource: `babylon_context`
  - Provides common context (current version, key concepts) for AI agents
  - Reduces need to repeatedly fetch basic information
- [ ] Create canonical response templates for common questions
- [ ] Add version-specific context handling
- [ ] Add resource subscriptions for documentation/source updates
  - Convert get_babylon_doc and get_babylon_source from tools to resources
  - Implement file watching for repository changes
  - Send resource update notifications to subscribed clients

---

## Phase 4: Feedback Collection System
**Goal**: Allow users to provide feedback on search result usefulness

### 4.1 Database Design
- [ ] Choose database (SQLite for simplicity, PostgreSQL for production scale)
- [ ] Design schema for:
  - Search queries and returned results
  - User feedback (usefulness scores, relevance ratings)
  - Query-result effectiveness mappings
  - Anonymous session tracking

### 4.2 Feedback Submission
- [ ] Implement MCP tool: `provide_feedback`
  - Input: result identifier, query, usefulness score (1-5), optional comment
  - Output: confirmation and feedback ID
- [ ] Store feedback with query context
- [ ] Implement basic spam prevention
- [ ] Add feedback submission via Express REST API (optional web interface)

### 4.3 Feedback Analytics Foundation
- [ ] Create queries for feedback aggregation
- [ ] Implement basic feedback score calculations
- [ ] Design feedback reporting structure
- [ ] Add feedback data export capabilities

---

## Phase 5: Learning & Ranking Optimization
**Goal**: Use collected feedback to improve search result relevance

### 5.1 Feedback-Driven Ranking
- [ ] Integrate feedback scores into search ranking algorithm
- [ ] Implement boost factors for highly-rated results
- [ ] Add penalty factors for low-rated results
- [ ] Create decay function (recent feedback weighted higher)
- [ ] Test ranking improvements with historical queries

### 5.2 Query Understanding
- [ ] Analyze successful searches to identify patterns
- [ ] Implement query expansion based on feedback
- [ ] Add synonym detection for common Babylon.js terms
- [ ] Create query-to-topic mapping
- [ ] Implement "did you mean" suggestions

### 5.3 Result Quality Monitoring
- [ ] Track result click-through rates (if applicable)
- [ ] Identify zero-result queries for improvement
- [ ] Monitor feedback trends over time
- [ ] Create alerts for sudden quality drops
- [ ] Implement A/B testing framework for ranking changes

---

## Phase 6: Feature Requests & Community Engagement
**Goal**: Enable users to suggest improvements and vote on feature requests

### 6.1 Suggestion Collection
- [ ] Extend database schema for feature requests/improvements
- [ ] Implement MCP tool: `submit_suggestion`
  - Input: suggestion text, category (documentation, example, feature)
  - Output: suggestion ID for tracking
- [ ] Add suggestion categorization and tagging
- [ ] Implement duplicate detection for similar suggestions

### 6.2 Voting System
- [ ] Implement MCP tool: `vote_on_suggestion`
  - Input: suggestion ID, vote (up/down)
  - Output: updated vote count
- [ ] Design anonymous voting with abuse prevention
- [ ] Add vote weight based on user activity (optional)
- [ ] Implement vote aggregation and trending calculations

### 6.3 Suggestion Discovery
- [ ] Implement MCP tool: `browse_suggestions`
  - Input: filters (category, status, sort order)
  - Output: paginated list of suggestions with vote counts
- [ ] Add search within suggestions
- [ ] Create status tracking (new, under review, implemented, rejected)
- [ ] Add suggestion updates and resolution tracking

### 6.4 Community Dashboard (Optional)
- [ ] Create web interface for browsing suggestions
- [ ] Add suggestion detail pages with discussion
- [ ] Implement suggestion status updates by maintainers
- [ ] Add notification system for suggestion updates

---

## Phase 7: Advanced Features & Quality
**Goal**: Enhance capabilities and ensure production readiness

### 7.1 Multi-Version Support
- [ ] Detect Babylon.js versions in Documentation repo
- [ ] Index documentation for multiple versions separately
- [ ] Add version parameter to search tools
- [ ] Implement version comparison capabilities
- [ ] Create migration guides between versions

### 7.2 Code-Aware Search
- [ ] Implement code pattern search in examples
- [ ] Add TypeScript/JavaScript syntax understanding
- [ ] Create API signature search
- [ ] Add "find usage examples" for specific APIs
- [ ] Implement code-to-documentation linking

### 7.3 Performance & Scalability
- [ ] Optimize search query performance (< 500ms p95)
- [ ] Implement connection pooling for database
- [ ] Add request queuing for high load
- [ ] Optimize memory usage for large indexes
- [ ] Implement graceful degradation under load

### 7.4 Testing & Quality Assurance
- [ ] Write unit tests for core indexing and search logic
- [ ] Create integration tests for MCP tools
- [ ] Add end-to-end tests for critical workflows
- [ ] Implement regression testing for ranking changes
- [ ] Add performance benchmarks and monitoring

---

## Phase 8: Deployment & Operations
**Goal**: Make the server production-ready and maintainable

### 8.1 Deployment Infrastructure
- [ ] Create Dockerfile for containerization
- [ ] Set up docker-compose for local development
- [ ] Implement configuration management (environment variables)
- [ ] Create database migration system
- [ ] Add health check endpoints

### 8.2 Automation & CI/CD
- [ ] Set up GitHub Actions for testing
- [ ] Implement automated builds and releases
- [ ] Create automated documentation update workflow
- [ ] Add automated index rebuilding schedule
- [ ] Implement version tagging and release notes

### 8.3 Monitoring & Observability
- [ ] Add structured logging (JSON format)
- [ ] Implement metrics collection (Prometheus-compatible)
- [ ] Create performance dashboards
- [ ] Add error tracking and alerting
- [ ] Implement trace logging for debugging

### 8.4 Documentation & Onboarding
- [ ] Write installation guide for MCP server
- [ ] Create configuration documentation
- [ ] Document all MCP tools with examples
- [ ] Add troubleshooting guide
- [ ] Create developer contribution guide

---

## Technical Architecture Decisions

### MCP Implementation
- **SDK**: @modelcontextprotocol/sdk (official TypeScript SDK)
- **Transport**: HTTP with Server-Sent Events (SSE) on port 3001
- **MCP Endpoint**: `/mcp/sse`
- **Tools**: search_babylon_docs, get_babylon_doc, search_babylon_examples, provide_feedback, submit_suggestion, vote_on_suggestion, browse_suggestions
- **Resources**: babylon_context (common framework information)

### Search & Indexing (✅ Implemented)
- **Vector Database**: LanceDB for vector storage and similarity search
- **Embedding Model**: @xenova/transformers with Xenova/all-MiniLM-L6-v2 (local, no API costs)
- **Document Parser**: gray-matter for YAML frontmatter + markdown content extraction
- **Indexed Documents**: 745 markdown files from BabylonJS/Documentation repository
- **Search Features**: Semantic vector search with relevance scoring, category filtering, snippet extraction

### Data Storage (✅ Implemented)
- **Vector Database**: LanceDB stored in `./data/lancedb`
- **Document Storage**: Local clone of BabylonJS/Documentation in `./data/repositories/Documentation`
- **Indexed Fields**: title, description, keywords, category, breadcrumbs, content, headings, code blocks, playground IDs
- **Future**: Add Redis for query caching, implement incremental updates

### Token Optimization Strategy
- Return concise snippets by default (50-200 tokens)
- Offer detailed responses on demand
- Cache common context to avoid repetition
- Use efficient markdown formatting
- Implement smart content truncation

### Security & Privacy
- Anonymous feedback collection (no PII)
- Rate limiting on all MCP tools
- Input validation and sanitization
- Secure database access patterns
- No authentication required (open access)

---

## Success Metrics

### Phase 1-2 (Core Functionality) ✅ ACHIEVED
- ✅ Documentation indexing: 100% of BabylonJS/Documentation repo (745 files indexed)
- ✅ Search implementation: LanceDB vector search with local embeddings operational
- ⏳ Search response time: Testing needed for p95 latency
- ⏳ Search relevance: Initial tests successful, needs broader validation
- ⏳ Token efficiency: Needs measurement and optimization

### Phase 3-5 (Optimization & Feedback)
- Cache hit rate: > 60%
- Feedback collection rate: > 5% of searches
- Ranking improvement: Increase in positive feedback over time
- Query success rate: < 5% zero-result queries

### Phase 6-8 (Community & Production)
- Suggestion collection: Active community participation
- Uptime: > 99%
- Documentation freshness: < 24 hour lag from repo updates
- Test coverage: > 80% of core functionality

---

## Future Enhancements (Post-Launch)

- Integration with Babylon.js GitHub issues for additional context
- Real-time collaborative debugging sessions
- Visual search for shader/rendering effects
- Performance optimization recommendations based on best practices
- Integration with TypeScript Language Server for IDE features
- Multi-language documentation support
- Community-contributed solutions and patterns library
- Interactive tutorial generation based on user goals