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

Build scalable, secure LLM applications with the Model Context Protocol and design modular, context-aware multi-agent systems for real-world deployment

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Key Features

• 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

Book 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.

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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

Who this book is 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.

Table of Contents

1. Introduction to the Model Context Protocol
2. Theoretical Foundations of Multi-Agent Systems
3. The MCP for Non-Technical Readers
4. MCP Components and Interfaces
5. MCP Architecture Overview
6. Server-Side Implementation
7. Client-Side Integration
8. MCP Security Model
9. MCP Performance Optimization
10. MCP and Multi-Agent Systems
11. MCP for Retrieval-Augmented Generation
12. Integrating MCP with LangChain
13. Integrating MCP with AutoGen
14. MCP for Enterprise Knowledge Management
15. MCP for Personalization and Recommendation Systems

(N.B. Please use the Read Sample option to see further chapters)

Naveen Krishnan is a Data and AI Solutions Engineer with over 17 years of experience delivering enterprise-grade systems across retail, banking, healthcare, and manufacturing. As an AI Lead at Microsoft, a Fellow of BCS, and a Senior Member of IEEE, he has designed and deployed large-scale RAG and multi-agent systems using LangChain, AutoGen, and MCP. A judge at global NASA Space Apps and Microsoft hackathons and the author of 35+ technical publications, he specializes in secure, scalable AI architectures and responsible AI practices for real-world deployment.