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Model Context Protocol for LLMs
Model Context Protocol for LLMs

Model Context Protocol for LLMs: Build secure, scalable, and context-aware AI agents using a standardized protocol

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Profile Icon Naveen Krishnan
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$39.59 $43.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
eBook Feb 2026 436 pages 1st Edition
eBook
$39.59 $43.99
Paperback
$54.99
eBook + Subscription
$24.99 Monthly
Arrow left icon
Profile Icon Naveen Krishnan
Arrow right icon
$39.59 $43.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
eBook Feb 2026 436 pages 1st Edition
eBook
$39.59 $43.99
Paperback
$54.99
eBook + Subscription
$24.99 Monthly
eBook
$39.59 $43.99
Paperback
$54.99
eBook + Subscription
$24.99 Monthly

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Model Context Protocol for LLMs

Part 1

Foundations and Concepts

In this first part of the book, you will develop a solid understanding of the Model Context Protocol and the problems it was designed to solve. We begin by exploring the evolution of AI systems and the disconnected model problem that has created integration headaches across the industry. You will then examine the theoretical foundations of multi-agent systems, understanding how agency, coordination, and communication work at a conceptual level. The final chapter in this part offers an accessible, jargon-free overview of MCP for readers who want to build intuition before diving into implementation. By the end of this part, you will have the conceptual grounding needed to understand why MCP exists and how it fits into the broader AI landscape.

This part of the book includes the following chapters:

  • Chapter 1, Introduction to the Model Context Protocol
  • Chapter 2, Theoretical Foundations of Multi-Agent Systems
  • Chapter 3, The MCP for Non-Technical Readers
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Key benefits

  • Build modular, production-ready AI agents using the Model Context Protocol (MCP)
  • Integrate MCP with LangChain, AutoGen, and RAG for multi-agent collaboration
  • Apply security, performance optimization, and evaluation patterns for real-world deployment

Description

Modern 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. You'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. You'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. Written 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. *Email sign-up and proof of purchase required

Who is this book for?

AI/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.

What you will learn

  • Understand the MCP architecture and standardized primitives
  • Implement resource and tool providers in Python
  • Connect LangChain and AutoGen to MCP pipelines
  • Secure agent interactions using authentication and access control
  • Add RAG pipelines with shared contextual memory
  • Apply authentication, TLS, and access control models
  • Optimize performance with caching and async patterns
  • Evaluate and benchmark MCP systems for production readiness

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 28, 2026
Length: 436 pages
Edition : 1st
Language : English
ISBN-13 : 9781806662265
Languages :
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Product Details

Publication date : Feb 28, 2026
Length: 436 pages
Edition : 1st
Language : English
ISBN-13 : 9781806662265
Languages :
Tools :

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Table of Contents

28 Chapters
Part 1: Foundations and Concepts Chevron down icon Chevron up icon
Chapter 1: Introduction to the Model Context Protocol Chevron down icon Chevron up icon
Chapter 2: Theoretical Foundations of Multi-Agent Systems Chevron down icon Chevron up icon
Chapter 3: The MCP for Non-Technical Readers Chevron down icon Chevron up icon
Part 2: Architecture and Core Implementation Chevron down icon Chevron up icon
Chapter 4: MCP Components and Interfaces Chevron down icon Chevron up icon
Chapter 5: MCP Architecture Overview Chevron down icon Chevron up icon
Chapter 6: Server-Side Implementation Chevron down icon Chevron up icon
Chapter 7: Client-Side Integration Chevron down icon Chevron up icon
Part 3: Security and Performance Chevron down icon Chevron up icon
Chapter 8: MCP Security Model Chevron down icon Chevron up icon
Chapter 9: MCP Performance Optimization Chevron down icon Chevron up icon
Part 4: Multi-Agent Systems and Framework Integration Chevron down icon Chevron up icon
Chapter 10: MCP and Multi-Agent Systems Chevron down icon Chevron up icon
Chapter 11: MCP for Retrieval-Augmented Generation Chevron down icon Chevron up icon
Chapter 12: Integrating MCP with LangChain Chevron down icon Chevron up icon
Chapter 13: Integrating MCP with AutoGen Chevron down icon Chevron up icon
Part 5: Real-World Applications Chevron down icon Chevron up icon
Chapter 14: MCP for Enterprise Knowledge Management Chevron down icon Chevron up icon
Chapter 15: MCP for Personalization and Recommendation Systems Chevron down icon Chevron up icon
Chapter 16: MCP for Multimodal Applications Chevron down icon Chevron up icon
Part 6: Evaluation, Optimization, and the Future Chevron down icon Chevron up icon
Chapter 17: MCP Evaluation Methodologies Chevron down icon Chevron up icon
Chapter 18: Performance Benchmarks and Testing Chevron down icon Chevron up icon
Chapter 19: Optimization Strategies and Performance Tuning Chevron down icon Chevron up icon
Chapter 20: Future Directions and Emerging Trends Chevron down icon Chevron up icon
Chapter 21: Unlock Access to the Code Bundle and the PDF Version Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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