EuConquisto Composer MCP

# Resource Constraints Assessment

## Task Overview
**Assigned to**: Claude Desktop  
**Priority**: High  
**Timeline**: Strategic planning phase  
**Dependencies**: None

## Objective
Determine the server resource requirements and deployment constraints for the EuConquisto Composer MCP server to inform architectural decisions and scalability planning.

## Background Context
The MCP server uses Playwright browser automation which has significant resource requirements. Current implementation shows:
- Browser automation with Chromium instances
- Potential memory leaks in long-running scenarios
- No resource pooling or cleanup strategies
- Unknown server environment constraints

## Key Questions to Address

### 1. Server Environment Constraints
- What server resources are available for deployment?
- Memory limitations for browser automation
- CPU constraints for Chromium instances
- Network bandwidth considerations
- Operating system and containerization support

### 2. Browser Automation Resource Requirements
- How many concurrent browser instances can be supported?
- Memory usage per Chromium instance (~100-200MB typical)
- Browser lifecycle management requirements
- Resource cleanup strategies needed

### 3. Scalability Considerations
- Expected concurrent user load
- Peak usage patterns
- Horizontal vs vertical scaling options
- Load balancing requirements

### 4. Performance Targets
- Acceptable response times for composition creation
- Maximum timeout thresholds
- Resource usage monitoring requirements
- Performance degradation thresholds

## Deliverables Expected

### 1. Resource Requirements Document
- Minimum server specifications
- Recommended server configurations
- Memory and CPU requirements per concurrent user
- Network and storage requirements

### 2. Deployment Architecture Recommendations
- Containerization strategy (Docker/Kubernetes)
- Resource pooling approaches
- Monitoring and alerting requirements
- Auto-scaling policies

### 3. Performance Constraints
- Maximum concurrent users supported
- Response time targets
- Resource usage thresholds
- Failover and recovery strategies

## Technical Considerations

### Current Implementation Issues
- No browser instance pooling
- Missing resource cleanup in error scenarios
- Potential memory leaks in long-running processes
- No connection limits or queuing

### Proposed Solutions to Evaluate
- Browser instance pooling with max limits
- Resource cleanup with timeouts
- Connection queuing for high load
- Health checks and auto-recovery

## Success Criteria
- [ ] Clear resource requirements documented
- [ ] Deployment architecture defined
- [ ] Performance targets established
- [ ] Monitoring strategy outlined
- [ ] Scaling plan created

## Follow-up Actions
Results will inform:
- Browser automation architecture decisions
- Deployment environment selection
- Performance optimization priorities
- Infrastructure provisioning requirements

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**Note**: This assessment is critical for determining the feasibility of the current browser automation approach and planning the production deployment strategy.