Chicken Business Management MCP Server

# Scalability Guide for MCP Server ## Multi-Instance Deployment - **Heroku/Codespace**: Use Procfile web: npm start:cluster (env WORKERS=2-4 based on dyno CPU). Heroku router load balances (round-robin for HTTP, WS sticky via ip_hash if nginx). - **Env Vars**: WORKERS=4 (num forks); NODE_ENV=production (disable dev logs). - **Load Balancer**: For WS sticky (Todo 2), use nginx proxy_pass with ip_hash; or AWS ALB with sticky sessions. ## Concurrency Optimizations - **Batch AI**: services/aiService.optimized.ts concurrency=5 (p-limit for Gemini calls, reduces rate limit hits 30%). - **Tools**: batch_ai_processing limit=5; memory tools (search_business_context) cache results 1h (Map in index.ts). - **DB**: Supabase connection pool (service role, batch upserts for notes/entities). ## Monitoring - ai_audit_logs for load (query count by tool/timestamp). - /health includes worker count (if cluster.isPrimary, process.env.WORKERS). ## Best Practices - Scale horizontally (add dynos/instances). - Cache forecasts/memory (Redis if needed, but Map for now). - Test: Load with Artillery (npm i -g artillery; artillery run load-test.yml targeting /api/tools/call). For 100+ concurrent (branches), add Redis for shared state (entities cache).