{"product_id":"9781806662272","title":"Model Context Protocol for LLMs : Build secure, scalable, and context-aware AI agents using a standardized protocol","description":"\u003cp\u003eBuild scalable, secure LLM applications with the Model Context Protocol and design modular, context-aware multi-agent systems for real-world deployment\u003c\/p\u003e\n\n\u003cp\u003eFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eBuild modular, production-ready AI agents using the Model Context Protocol (MCP)\n\u003cbr\u003eIntegrate MCP with LangChain, AutoGen, and RAG for multi-agent collaboration\n\u003cbr\u003eApply security, performance optimization, and evaluation patterns for real-world deployment\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionModern LLM applications often fail due to weak context management, fragile tool integration, and poorly coordinated agents. To address these challenges, this book provides a practical blueprint for building reliable, scalable AI systems using the Model Context Protocol (MCP), an open standard for interoperable AI architectures.\n\u003cbr\u003eYou'll explore why context is the missing layer in many AI deployments and how MCP formalizes it. Through clear explanations and practical examples, you'll design modular components such as resource providers, tool providers, gateways, and standardized interfaces. You'll also integrate MCP with LangChain, AutoGen, and RAG pipelines to build collaborative, context-aware multi-agent systems.\n\u003cbr\u003eYou'll learn how to apply MCP to multimodal applications, personalization engines, and enterprise knowledge management solutions, while evaluating and benchmarking implementations for production readiness and implementing authentication, authorization, and scaling strategies for secure cloud deployments.\n\u003cbr\u003eWritten by a data and AI solutions engineer with over 17 years of experience at Microsoft and Fortune 500 organizations, this guide combines architectural depth with hands-on implementation. By the end, you'll be able to design, build, and deploy secure, reusable MCP-based LLM systems that scale confidently in production.\u003c\/p\u003e\n\n\u003cp\u003e*Email sign-up and proof of purchase required\n\u003cbr\u003e What you will learn\u003c\/p\u003e\n\n\u003cp\u003eUnderstand the MCP architecture and standardized primitives\n\u003cbr\u003eImplement resource and tool providers in Python\n\u003cbr\u003eConnect LangChain and AutoGen to MCP pipelines\n\u003cbr\u003eSecure agent interactions using authentication and access control\n\u003cbr\u003eAdd RAG pipelines with shared contextual memory\n\u003cbr\u003eApply authentication, TLS, and access control models\n\u003cbr\u003eOptimize performance with caching and async patterns\n\u003cbr\u003eEvaluate and benchmark MCP systems for production readiness\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forAI\/ML engineers, software engineers, and solution architects building LLM-powered applications in production will benefit the most from this book. Cloud architects and platform engineers designing AI infrastructure will also find it valuable. If you're looking for a standardized, modular, and secure approach to managing context across agents and tools, this guide is for you. Intermediate Python skills, a working knowledge of LLM concepts and REST APIs, and familiarity with system design patterns are expected.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":52443824521527,"sku":"00000_00000_00000_00000","price":297.93,"currency_code":"MYR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0962\/3548\/7543\/files\/9781806662272-1.jpg?v=1783241657","url":"https:\/\/kinokuniya.com.my\/products\/9781806662272","provider":"Books Kinokuniya Malaysia","version":"1.0","type":"link"}