MCPs: The Choose-Your-Own-Adventure Games of Enterprise AI

When people hear “Model Context Protocol (MCP),” it sounds abstract and technical—like plumbing for AI systems. But if you strip it down, MCPs are really just goal-oriented choose-your-own-adventure games written for AI.

The parallel is useful, because it highlights the biggest design challenge emerging in MCP work today: the AI user experience, or AIUX.

MCP as a Goal-Oriented Game

In a classic choose-your-own-adventure book, the structure looks like this:

• The Goal: Rescue the treasure, defeat the villain, escape the maze.
• The Routes: Each decision branches into new scenarios.
• The Player: You, flipping pages, hoping to pick the right path.

MCPs map almost directly onto that structure:

• The Goal: Complete a business task (file a report, retrieve analytics, process an invoice).
• The Routes: A set of exposed tools, APIs, and prompts that the AI can call.
• The Player: An LLM, making sequential choices about which tool to use, what input to provide, and what to do with the response.

Success is reaching the right “ending” (task completed). Failure is ending up in a loop, hitting a dead end, or fumbling with the wrong tool call.

Why Complexity Breaks the Game

The recent MCP-Universe benchmark showed that even top-tier LLMs are only succeeding 30–45% of the time on complex, multi-tool tasks. That’s because:

• Too many branches → The model gets lost in the options.
• Unclear tool rules → The API call syntax is confusing, inconsistent, or undocumented.
• Ambiguous states → The model doesn’t know whether it “won” or needs to keep going.

This is the equivalent of a badly written adventure book—where every choice leads to obscure paths, contradictory outcomes, or impossible puzzles. No surprise the AI fails.

AIX = Clean Paths for the AI Experience

If you think of MCPs as Choose-Your-Own-Adventure games, then good AIX is about writing clean paths through the story.

That means:

• Consistent tool design → predictable input/output formats, so the AI knows what to expect.
• Clear affordances → obvious next steps, no hidden or ambiguous branches.
• Error-tolerant routes → paths that let the AI recover from a mistake instead of hard-crashing.
• Goal signaling → explicit indicators when a task is done, so the AI doesn’t wander forever.
• Most importantly its about clear route signaling, where is the AI in the journey and where do you need to go.

In human UX, we obsess over reducing friction for users. AIX demands the same: reduce friction for the AI player so it can reach success more reliably.

The Big Takeaway

The lesson from MCP-Universe isn’t just that AIs struggle with complex orchestration. It’s that design matters, people struggle with bad design as well. A good MCP is less like a maze and more like a guided adventure, where the branches are structured to make success the natural outcome. Even is there is a dragon along the way.