"MCP servers: giving models safe access to your systems"
The moment an AI assistant needs to see your CRM, your database, or your ticketing system, someone has to build the bridge — and ad-hoc glue code is how that bridge ends up unauditable. MCP standardises the plumbing. What it doesn't standardise is judgement: tool design and auth boundaries are still on you.
The demo assistant answered questions beautifully. Then someone asked it about a real customer, and the honest answer was that it couldn't see your CRM, your database, or your ticketing system — so a developer wrote some glue code, then more glue for the next integration, and within months you had five bespoke bridges between a language model and your production systems, each with its own auth handling and none of them audited. This is the problem MCP exists to solve, and also the problem it only half solves.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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