MCP tutorials, production guides, and AI agent integration deep-dives.
A career in digital technology taught me to reinvent myself every few years. This time the challenge was enterprise AI governance — and I decided to test how far one person could push a real product using AI-assisted development.
As AI models proliferate inside teams, data access becomes a governance problem. One MCP gateway controls who accesses what, which tools employees can use, and carries context across every session — regardless of which model you're using.
We ran a self-audit before launch and found seven serious issues — Vault in dev mode, hardcoded admin creds, a gateway signing tokens with the default dev secret. Here's every finding and how we fixed it.
Tamper-evident audit logs, HMAC-signed webhooks, Stripe billing, BYOIDP, IP allowlists, GraphQL schema discovery, MCP versioning with rollback — a full rundown of everything added since the initial beta.
Step-by-step guide to exposing your Postgres database as callable MCP tools for Claude Desktop, Cursor, or any MCP-compatible AI agent — no custom server code required.
How the Model Context Protocol changes SaaS integration — and why a managed MCP server is the right foundation for AI-native products in 2026.
What it actually takes to run an MCP endpoint in production: auth, rate limiting, audit logs, key rotation, and the failure modes no one talks about.