EuConquisto Composer MCP

# User Volume & Scaling Analysis ## Task Overview **Assigned to**: Claude Desktop **Priority**: High **Timeline**: Strategic planning phase **Dependencies**: Resource constraints assessment ## Objective Analyze expected user volume patterns and define scaling requirements for the EuConquisto Composer MCP server to ensure the architecture can handle growth from initial low volume to production scale. ## Background Context Current project status indicates: - Initially low user volume expected - Browser automation resource intensive (one Chromium instance per operation) - No horizontal scaling capabilities in current implementation - Need to plan for future growth without over-engineering ## Key Analysis Areas ### 1. User Volume Projections - Initial deployment user count (confirmed: initially low) - Growth trajectory over first 6 months - Peak usage patterns (daily/weekly/seasonal) - Concurrent user expectations - Geographic distribution of users ### 2. Usage Pattern Analysis - Composition creation frequency per user - Average session duration for content creation - Peak hours and load distribution - Batch vs real-time usage patterns - Educational content creation workflows ### 3. Scaling Architecture Requirements - Current capacity: How many concurrent users can single instance handle? - Scaling triggers: When to add resources/instances - Horizontal vs vertical scaling strategy - Load balancing requirements - Database/storage scaling needs ### 4. Performance Impact Assessment - User experience requirements under load - Acceptable degradation thresholds - Queue management for high demand periods - Error handling and retry strategies ## Technical Scaling Considerations ### Current Architecture Limitations - Single browser instance per operation - No connection pooling or queuing - Stateful browser sessions - No load distribution mechanism - Memory usage scales linearly with users ### Scaling Options to Evaluate #### Option 1: Vertical Scaling - Increase server resources (CPU/Memory) - Browser instance pooling - Connection queuing system - Resource optimization #### Option 2: Horizontal Scaling - Multiple MCP server instances - Load balancer with session affinity - Distributed browser automation - Shared session storage #### Option 3: Hybrid Approach - Auto-scaling based on demand - Dynamic resource allocation - Intelligent load distribution - Fallback mechanisms ## Questions to Address ### 1. Business Requirements - What defines "initially low volume"? (10 users? 100 users? 1000 users?) - Expected growth rate over first year - Budget constraints for infrastructure - Service level agreements (SLA) requirements ### 2. Technical Constraints - Maximum acceptable response time under load - Resource budget for scaling - Monitoring and alerting requirements - Disaster recovery needs ### 3. User Experience - Acceptable wait times for composition creation - Queue position visibility requirements - Graceful degradation strategies - Error recovery user flows ## Deliverables Expected ### 1. Scaling Strategy Document - User volume projections and assumptions - Scaling trigger points and thresholds - Resource allocation recommendations - Cost projections for different scales ### 2. Architecture Scaling Plan - Recommended scaling approach (vertical/horizontal/hybrid) - Implementation phases and timelines - Performance monitoring requirements - Capacity planning guidelines ### 3. Implementation Roadmap - Phase 1: Initial deployment (low volume) - Phase 2: Growth phase scaling - Phase 3: High-volume production - Technical debt and optimization windows ## Success Criteria - [ ] User volume projections documented - [ ] Scaling strategy defined - [ ] Performance targets established - [ ] Implementation phases planned - [ ] Monitoring strategy outlined - [ ] Cost estimates provided ## Risk Assessment Areas - Under-estimation of user growth - Over-engineering for unlikely scenarios - Browser automation scalability limits - Infrastructure cost surprises - Performance degradation under load ## Follow-up Actions Results will inform: - Browser automation architecture decisions - Infrastructure provisioning strategy - Performance optimization priorities - Development timeline and resources - Budget and cost planning --- **Note**: This analysis is essential for right-sizing the initial implementation while ensuring it can scale appropriately as user volume grows.