"Mastering the Model Context Protocol (MCP): Standardizing Tool Access, Context, and Permissions for AI Agents"

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As AI agents move from demos to production systems, the hardest problems are no longer just model quality—they are interoperability, permissions, context handling, and safe access to external tools. This book is written for experienced engineers, architects, platform teams, and advanced AI practitioners who need a rigorous understanding of the Model Context Protocol (MCP) as an emerging standard for connecting models to real-world systems.

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You will learn how MCP structures host-client-server interactions, how its core primitives—tools, resources, prompts, and sampling—shape reliable agent behavior, and how transport, session, schema, and validation choices affect interoperability across implementations. The book also goes deep on security-first design, OAuth-based authorization for remote servers, production-grade server and client architecture, observability, testing, compatibility strategy, and multi-server workflow composition. By the end, readers will be able to design, evaluate, and operate MCP-based systems with technical precision and sound judgment.

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Rather than offering a superficial tour, this book treats MCP as both a protocol and an architectural discipline. It assumes comfort with distributed systems, APIs, JSON-based contracts, and modern AI application design, and it distinguishes itself through its emphasis on trade-offs, failure modes, and long-term maintainability in fast-evolving agent ecosystems.

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Mastering the Model Context Protocol (MCP)

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