DollhouseMCP

--- name: token-estimation-guidelines type: memory description: Guidelines for estimating and optimizing token usage in auto-load memories version: 1.0.0 author: DollhouseMCP category: documentation tags: - tokens - optimization - guidelines - auto-load - performance triggers: - token-estimation - token-guidelines - optimize-tokens - token-budget - token-usage autoLoad: false priority: 999 retention: permanent privacyLevel: public searchable: true trustLevel: VALIDATED --- # Token Estimation Guidelines for Auto-Load Memories ## Understanding Tokens ### What Are Tokens? Tokens are the fundamental units that AI models process. Think of them as "word pieces": - **1 token** ≈ 4 characters in English - **1 token** ≈ 0.75 words (conservative estimate) - **100 tokens** ≈ 75 words ≈ 1 paragraph - **1,000 tokens** ≈ 750 words ≈ 1-2 pages **Examples**: ``` "Hello, world!" = 4 tokens "The quick brown fox jumps" = 5 tokens "DollhouseMCP" = 3 tokens (Doll-house-MCP) ``` ### Why Tokens Matter for Auto-Load Auto-load memories consume tokens at **every startup**: - **Token Budget**: Default 5,000 tokens (~3,750 words) - **Impact**: Larger memories = less room for other memories - **Performance**: More tokens = longer startup time - **Cost**: Some AI services charge per token **Goal**: Maximize value per token. ## DollhouseMCP Estimation Method ### The Formula ```typescript estimatedTokens = Math.ceil(wordCount / 0.75) ``` **Conservative estimate**: 1 token per 0.75 words ### Example Calculation ``` Content: "The quick brown fox jumps over the lazy dog" Word count: 9 words Estimated tokens: 9 / 0.75 = 12 tokens ``` ### Accuracy - **Conservative**: Tends to overestimate slightly - **English-optimized**: Best for English text - **Code-aware**: May underestimate for code-heavy content **Comparison to other estimators**: - DollhouseMCP: 12 tokens (conservative) - Actual (Claude): 10-11 tokens (varies by model) - OpenAI tiktoken: 9-10 tokens **Recommendation**: Use DollhouseMCP estimate for budgeting, actual usage will be slightly lower. ## Token Size Categories ### Micro (0-500 tokens) **~0-375 words, ~0-0.5 pages** **Best For**: - Quick reference cards - Cheat sheets - Key definitions - Contact lists **Example**: ```yaml name: team-contacts tokens: 350 content: | # Team Contacts - PM: Jane (@jane) - Tech Lead: Bob (@bob) - DevOps: Alice (@alice) ## Escalation 1. Team Lead → Director → VP 2. After hours: On-call rotation (PagerDuty) ``` ### Small (500-1,500 tokens) **~375-1,125 words, ~0.5-1.5 pages** **Best For**: - Coding standards summaries - API endpoint lists - Configuration options - Brief project overviews **Example**: ```yaml name: coding-standards tokens: 1,200 content: | # Coding Standards ## TypeScript - Use strict mode - No `any` types - Prefer interfaces over types ## Testing - Coverage >80% - Jest for unit tests - Playwright for E2E ## PR Process 1. Feature branch from main 2. Write tests first 3. Get 2 approvals 4. Squash merge ``` ### Medium (1,500-5,000 tokens) **~1,125-3,750 words, ~1.5-5 pages** **Best For**: - Project architecture overviews - Domain knowledge primers - Team onboarding guides - Detailed API references **Example**: ```yaml name: project-architecture tokens: 3,500 content: | # Project Architecture ## System Overview MyApp is a 3-tier web application... ## Components ### Frontend (React) - User interface - State management (Redux) - API client ### Backend (Node.js) - REST API (Express) - Business logic - Authentication (JWT) ### Database (PostgreSQL) - User data - Transaction logs - Analytics ## Deployment - Dev: AWS ECS (fargate) - Staging: AWS ECS (fargate) - Prod: AWS ECS (EC2 reserved) [... detailed sections ...] ``` ### Large (5,000-10,000 tokens) **~3,750-7,500 words, ~5-10 pages** **Best For**: - Comprehensive documentation - Extensive domain knowledge - Multi-project context **Warning**: May trigger warnings, consider splitting. **Example**: ```yaml name: healthcare-domain-knowledge tokens: 7,800 content: | # Healthcare Domain Knowledge ## Medical Terminology [Extensive glossary...] ## Regulatory Compliance [HIPAA, GDPR, etc...] ## Clinical Workflows [Patient intake, treatment, discharge...] ## Coding Systems [ICD-10, CPT, SNOMED...] ``` ### Very Large (10,000+ tokens) **~7,500+ words, ~10+ pages** **Not Recommended for Auto-Load** **Why**: - Exceeds default budget alone - Slows startup significantly - Likely contains unnecessary detail **Solution**: Split into focused memories or use on-demand loading. ## Optimization Strategies ### Strategy 1: Use Bullet Points **Before** (verbose): ``` The application uses a three-tier architecture. The frontend is implemented using React and handles all user interactions. The backend is built with Node.js and Express and provides a REST API for the frontend to consume. The database layer uses PostgreSQL to store persistent data. ``` **Tokens**: ~60 **After** (bullet points): ``` Architecture: - Frontend: React (user interface) - Backend: Node.js + Express (REST API) - Database: PostgreSQL (persistent storage) ``` **Tokens**: ~30 (50% reduction) ### Strategy 2: Remove Examples **Before**: ``` Use TypeScript strict mode. Bad: function add(a, b) { return a + b; } Good: function add(a: number, b: number): number { return a + b; } ``` **Tokens**: ~40 **After**: ``` Use TypeScript strict mode (type all parameters and returns). ``` **Tokens**: ~12 (70% reduction) ### Strategy 3: Link Instead of Embed **Before**: ``` [Paste entire API documentation - 5,000 tokens] ``` **After**: ``` API Docs: https://docs.company.com/api Quick Reference: - GET /users - List users - POST /users - Create user - PUT /users/:id - Update user - DELETE /users/:id - Delete user ``` **Tokens**: ~50 (99% reduction) ### Strategy 4: Use Abbreviations **Before**: ``` The application programming interface endpoint for creating a new user account requires authentication via JSON Web Token in the Authorization header following the Bearer authentication scheme. ``` **Tokens**: ~40 **After**: ``` User creation API endpoint requires JWT Bearer auth in Authorization header. ``` **Tokens**: ~15 (62% reduction) ### Strategy 5: Remove Redundancy **Before**: ``` The frontend uses React. React is a JavaScript library for building user interfaces. We chose React because it's popular and has a large ecosystem of libraries and tools. ``` **Tokens**: ~35 **After**: ``` Frontend: React (popular, large ecosystem) ``` **Tokens**: ~8 (77% reduction) ### Strategy 6: Structured Lists Instead of Prose **Before**: ``` Our deployment process starts with creating a feature branch, then you write your code and tests, after that you open a pull request and get two approvals, and finally you merge to main which triggers automatic deployment to staging and then after QA approval it goes to production. ``` **Tokens**: ~60 **After**: ``` Deployment: 1. Create feature branch 2. Write code + tests 3. Open PR (2 approvals required) 4. Merge → staging (auto) 5. QA approval → production ``` **Tokens**: ~30 (50% reduction) ## Token Budgeting ### Default Budget (5,000 tokens) **Recommended Distribution**: ```yaml # System baseline (1,000 tokens) dollhousemcp-baseline-knowledge: 1,000 # Organizational context (1,500 tokens) company-coding-standards: 800 security-policies: 700 # Team context (1,500 tokens) team-patterns: 1,000 domain-knowledge: 500 # Project context (1,000 tokens) project-architecture: 600 current-sprint: 400 # Total: 5,000 tokens ``` ### Expanded Budget (10,000 tokens) For larger teams or complex projects: ```yaml autoLoad: maxTokenBudget: 10000 ``` **Recommended Distribution**: ```yaml # System (1,000) dollhousemcp-baseline-knowledge: 1,000 # Organizational (2,500) company-standards: 1,200 security-compliance: 800 architecture-principles: 500 # Team (3,500) team-patterns: 1,500 domain-knowledge: 1,000 api-conventions: 1,000 # Project (2,500) project-architecture: 1,500 current-sprint: 600 recent-decisions: 400 # Reference (500) error-codes: 300 common-commands: 200 # Total: 10,000 tokens ``` ### Aggressive Budget (15,000+ tokens) **Warning**: May impact startup performance. **Use Cases**: - Highly specialized domains (medical, legal, financial) - Large enterprise with complex context - Documentation-heavy projects **Recommendation**: Monitor startup time, consider splitting into multiple smaller memories instead. ## Measuring Token Usage ### Method 1: Validation Command ```bash dollhouse validate memory my-memory # Output includes: # "Estimated tokens: 1,234" ``` ### Method 2: Startup Logs ```bash # Look for auto-load summary in logs: # "[ServerStartup] Auto-load complete: 5 memories loaded (~3,200 tokens)" ``` ### Method 3: Bulk Analysis ```bash # Get token counts for all auto-load memories for memory in $(dollhouse list memories --filter autoLoad=true --json | jq -r '.[].name'); do echo "Memory: $memory" dollhouse validate memory "$memory" | grep "Estimated tokens" done ``` ### Method 4: Configuration Review ```bash # Check current budget dollhouse config show autoLoad.maxTokenBudget # Check budget utilization dollhouse config show autoLoad | grep -E "(maxTokenBudget|totalLoaded)" ``` ## Performance Impact ### Startup Time Token count affects startup time: - **1,000 tokens**: +5ms - **5,000 tokens**: +25ms (default) - **10,000 tokens**: +50ms - **20,000 tokens**: +100ms (not recommended) **Guideline**: Keep total under 10,000 tokens for fast startup. ### Memory Usage Each token consumes ~4 bytes in memory: - **5,000 tokens**: ~20 KB - **10,000 tokens**: ~40 KB - **50,000 tokens**: ~200 KB **Guideline**: Memory usage is negligible for typical budgets. ### Cost Impact (for paid AI services) Some AI services charge per token: - **Claude**: $0.015 per 1K tokens (input) - **GPT-4**: $0.03 per 1K tokens (input) **Example**: - 5,000 tokens per startup - 100 startups per day - 500,000 tokens per day - Claude cost: $7.50/day = $225/month **Guideline**: For high-frequency usage, optimize aggressively. ## Troubleshooting ### Issue 1: Budget Exceeded **Symptom**: "Token budget reached, loaded X memories, skipping remaining Y" **Diagnosis**: ```bash # List auto-load memories with estimates dollhouse list memories --filter autoLoad=true --format detailed ``` **Solutions**: 1. Increase budget: `autoLoad.maxTokenBudget: 10000` 2. Optimize large memories (see optimization strategies) 3. Reduce priority of less-important memories 4. Remove rarely-used memories from auto-load ### Issue 2: Memory Too Large Warning **Symptom**: "Memory 'xyz' is very large (~12,000 tokens)" **Diagnosis**: ```bash # Validate specific memory dollhouse validate memory xyz # Check word count wc -w ~/.dollhouse/portfolio/memories/xyz.yaml ``` **Solutions**: 1. Split into multiple focused memories 2. Remove verbose examples 3. Link to external docs instead of embedding 4. Use bullet points instead of paragraphs 5. Set `maxSingleMemoryTokens` limit ### Issue 3: Slow Startup **Symptom**: Startup takes >500ms **Diagnosis**: ```bash # Check total tokens loaded # Look in logs: "[ServerStartup] Auto-load complete: ... (~X tokens)" ``` **Solutions**: 1. Reduce total token budget 2. Optimize memories (target 60-80% budget utilization) 3. Remove auto-load flag from rarely-used memories 4. Consider caching (already implemented in v1.9.25+) ## Quick Reference | Size | Tokens | Words | Pages | Best For | |------|--------|-------|-------|----------| | Micro | 0-500 | 0-375 | 0-0.5 | Quick reference, contact lists | | Small | 500-1.5K | 375-1.1K | 0.5-1.5 | Standards summary, API lists | | Medium | 1.5K-5K | 1.1K-3.8K | 1.5-5 | Architecture, domain knowledge | | Large | 5K-10K | 3.8K-7.5K | 5-10 | Comprehensive docs (split recommended) | | Very Large | 10K+ | 7.5K+ | 10+ | Not recommended for auto-load | ## Token Optimization Checklist - [ ] Use bullet points instead of paragraphs - [ ] Remove verbose examples (link to docs instead) - [ ] Abbreviate common terms (API, JWT, DB, etc.) - [ ] Use structured lists for processes - [ ] Remove redundant explanations - [ ] Link to external docs for details - [ ] Use tables for comparisons - [ ] Remove "filler" words (the, a, an, that, which) - [ ] Target 60-80% budget utilization - [ ] Review and optimize quarterly ## Related Documentation - [How to Create Custom Auto-Load Memories](./how-to-create-custom-auto-load-memories.yaml) - [Priority Best Practices for Teams](./priority-best-practices-for-teams.yaml) --- **Last Updated**: v1.9.25 (October 2025)