MCP for the Enterprise

AI assistants and agents are moving beyond conversation. They are calling tools, searching repositories, reading documents, and taking actions across enterprise systems.

The Model Context Protocol (MCP) provides a standard way to connect AI applications to those systems. But in an enterprise, connectivity is only the beginning. You still need to answer critical questions: Who is making the request? What are they allowed to do? Which identity reaches the backend? And can you prove what happened later?

MCP for the Enterprise shows how to build a secure, governed, production-oriented MCP architecture around those questions.

Using a hands-on reference implementation, you will build:

• A centralized MCP gateway with OAuth, dynamic client registration, PKCE, and per-user identity propagation
• A reusable framework for building and governing multiple MCP servers across enterprise systems
• Safe, bounded MCP tools with validation, allowlists, and deterministic authorization
• Human approval workflows for sensitive and high-risk agent actions
• Operational telemetry, audit records, agent memory, and evaluation
• Pre-LLM protection for secrets and PII
• Context optimization and token budgeting for long-running agent workflows
• Connections from clients such as Cursor and Claude through the governed gateway
• A production deployment architecture on Google Kubernetes Engine (GKE)
• A full-stack capstone that traces one enterprise investigation from user request to backend result and audit evidence

Who is this book for?

Platform engineers, security architects, AI infrastructure engineers, senior developers, enterprise architects, and technical leaders responsible for connecting AI agents to internal systems - especially in regulated, security-sensitive, or multi-team environments.

What makes this book different?

This is not only an introduction to MCP. Each chapter focuses on the controls required when MCP moves from a developer laptop into an enterprise environment: per-user identity, least privilege, authorization, safe tool design, approval, observability, durable state, evaluation, privacy, and production deployment.

Companion source code is available on GitHub so you can follow the architecture and labs end to end.

If your organization is connecting AI agents to internal systems and asking "Who is acting, what can they do, and can we prove it later?", this book provides a practical blueprint.