Zayne Turner

Distributed transactions fail partway through. Payment succeeds, then Salesforce times out. The guest is charged, but three systems hold stale state. In production, this happens constantly: a system times out, a connection drops mid-request, a user submits unexpected input. In distributed systems, these failures often mean a transaction completes in one system and fails in another—requiring reconciliation to restore consistency across all systems. What does this look like in composed MCP tools?

In the first two posts of this series, we explored why enterprise MCP needs compositional architecture and how to design skills that abstract complexity from the AI agent. But there's a question we haven't addressed: how do those tools actually execute at runtime? The Model Context Protocol defines how AI agents discover and invoke tools. But the protocol says nothing about persistent connections vs. stateless HTTP, session-cached state vs. source-of-truth lookups, or long-running processes vs.

In my previous post, I discussed how the biggest gap in enterprise MCP implementations isn't the protocol itself—it's the architectural decisions around it. Specifically, how teams treat MCP as "API gateway for LLMs" when they should be thinking about composable tool design. Today, I want to show you what composable skills-based tool design actually looks like in practice. Hotel Operations Case Study Let's start with a real scenario from a hotel management system.