restaurant-ai-mcp
by stefanogebara
Overview
AI-powered restaurant management platform focused on automating reservation handling, predicting no-shows, optimizing floor management, and providing comprehensive analytics using a multi-agent AI system and Model Context Protocol (MCP) tools.
Installation
cd mcp-server && npm install && npm run build && npm startEnvironment Variables
- AIRTABLE_API_KEY
- AIRTABLE_BASE_ID
- RESERVATIONS_TABLE_ID
- TABLES_TABLE_ID
- SERVICE_RECORDS_TABLE_ID
- CUSTOMER_HISTORY_TABLE_ID
- WAITLIST_TABLE_ID
- TWILIO_ACCOUNT_SID
- TWILIO_AUTH_TOKEN
- TWILIO_PHONE_NUMBER
- RESEND_API_KEY
- SUPABASE_URL
- SUPABASE_SERVICE_ROLE_KEY
- SUPABASE_ANON_KEY
- ML_ENDPOINT_URL
- ELEVENLABS_API_KEY
- ELEVENLABS_AGENT_ID
- CARTESIA_API_KEY
- STRIPE_SECRET_KEY
- STRIPE_WEBHOOK_SECRET
- SUBSCRIPTIONS_TABLE_ID
- VITE_SENTRY_DSN
- VITE_VERCEL_GIT_COMMIT_SHA
- VITE_API_URL
- VITE_SUPABASE_URL
- VITE_SUPABASE_ANON_KEY
- GOOGLE_CLOUD_PROJECT
- VERTEX_AI_LOCATION
- GEMINI_MODEL
- GEMINI_API_KEY
Security Notes
The system explicitly states 'No authentication layer observed' and 'No rate limiting visible on API endpoints' for its production APIs. This is a critical vulnerability, allowing any malicious actor to interact directly with the API, potentially leading to unauthorized data manipulation, resource exhaustion, or other forms of abuse. While input validation exists, and secrets are stored in environment variables (good practice), the fundamental lack of authentication and rate limiting makes the system unsafe for public-facing production deployment. The multi-agent architecture and integration with external LLM/AI services (ElevenLabs, Google ADK) introduce additional attack surfaces, particularly related to prompt injection if not strictly contained, though the current prompt is a system prompt.
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