Build a Multi-Agent App with MCP & MongoDB: Promotion Tycoon

How do you turn a vague “personal assistant” idea into a reliable multi-agent application? In this code-along, we’ll build Promotion Tycoon—an agentic workflow that helps you define promotion criteria, align goals and KPIs, and continuously score your projects against them. 

You’ll learn core patterns for agent role design, capability scoping and tool use, memory & state management, and quality control loops (reviewers, critics, guards). 

We’ll use an MCP (Model Context Protocol) server to expose tools and data sources to multiple cooperating agents—e.g., a Goal Analyzer, Criteria Generator (with web search), and Project Scorer—while persisting state in a database so the system improves over time. The focus is on portable design principles you can reuse in other domains: modeling tasks, routing work between agents, enforcing structured outputs, and measuring progress with reproducible scoring. You’ll leave with a working scaffold, a clear mental model of agent collaboration, and a checklist for taking an agent from a demo to something dependable.