Designing MCP systems for real teams

The Model Context Protocol lets AI assistants reach your real tools and data. The temptation is to expose everything. The better move is to expose a few high-leverage tools with sharp boundaries, strong access control, and real observability. A focused MCP system that an engineer can reason about beats a sprawling one that nobody trusts.

Start with the task, not the API surface

Do not ask “what can we expose?” Ask “what task are we trying to make AI good at?” The answer points to three or four tools, not thirty. I built a Trello MCP ahead of official support by scoping it to the handful of operations that actually mattered for the workflow. Narrow scope is what makes the system predictable.

Boundaries beat autonomy

Open-ended agent autonomy is a great demo and a poor production strategy. Scope each agent to a real, bounded task. Make the tool definitions explicit about what they do and do not touch. The system should fail in ways an engineer can predict and recover from.

Make it maintainable

An MCP system your engineers cannot extend is a liability. Design the patterns so adding the next tool is obvious. Add observability from the start: you want to see what the assistant called, with what arguments, and what came back. That is how you debug refusals, surprises, and silent failures.

The enterprise checklist

• Scope to a few high-leverage tools, not the whole API.
• Put access control at the tool boundary, not in the prompt.
• Log every tool call with arguments and results.
• Write tool descriptions an engineer would write.
• Leave patterns the team can extend without you.

This is the substance of multi-agent & MCP systems work. If you are building AI into developer tooling, see also why AI pilots stall before production.