If you've ever tried to run a Dungeons & Dragons session using just a standard LLM window, you know exactly where it falls apart.
You ask for a roll. The model simulates it. It feels okay until you realize the probabilistic math isn't quite hitting the mark, or worse, it forgets the nuances of advantage/disadvantage rules. Then comes the heavy lifting—calculating damage while factoring in resistances, vulnerabilities, and proficiency bonuses. This is where 'hallucinated randomness' becomes a thing. Most people think they can just prompt their way around this. They can't.
The issue isn't intelligence; it's delegation. You shouldn't be asking an LLM to perform arithmetic or logic checks that follow strict, deterministic rulebooks. You should be giving it a tool that does those calculations perfectly so the agent can focus on being the Dungeon Master.
I looked into how we bridge this gap using the Model Context Protocol (MCP). Instead of teaching an agent how to play D&D through massive system prompts (which eats tokens and invites errors), we provide specialized tools that act as the mechanical backbone of the game.
Beyond Basic Randomness
