Model Context Protocol for LLMs

About the Book

Learn to build composable and scalable LLM systems with the Model Context Protocol. Create context-rich, multi-agent AI apps with memory, orchestration, governance, and seamless LangChain or AutoGen integration. Key Features Build context-aware LLM systems using Model Context Protocol Integrate resource providers, tool agents, and context gateways Secure multi-agent orchestration across modular AI architectures Optimize performance with caching, async tasks, and profiling Connect MCP with Lang Chain, Auto Gen, and RAG framework Book DescriptionAI developers face a growing challenge: building intelligent systems that retain long-term memory, reason over dynamic context, and integrate safely with external tools. Model Context Protocol for LLMs provides a modern solution—offering an open, modular architecture to construct scalable LLM agents with structured context exchange. This book equips you with a complete hands-on journey to MCP. You’ll implement the protocol’s key components—resource providers, tool providers, and gateways—then use these to orchestrate agents, chain workflows, and add context-aware behavior. You’ll also learn how MCP integrates seamlessly with LangChain, AutoGen, RAG systems, and multimodal applications. Security and governance are covered in depth, helping you build privacy-compliant, threat-resistant AI apps. You’ll explore caching, async tasks, load balancing, and scaling strategies for real-world readiness. With a continuous hands-on project, MCP becomes more than a standard—it becomes a blueprint for production-grade LLM development. What you will learn Understand why disconnected agents fail and how MCP solves it Design standardized, context-aware interfaces with MCP Implement MCP components with Python and cloud-native tools Build LangChain and AutoGen workflows powered by MCP Create scalable multi-agent systems that collaborate in real time Secure agent interactions using authentication and access control Optimize performance across client and server MCP deployments Apply MCP to personalization, RAG, and multimodal AI systems Who this book is forAI/ML engineers, solution architects, MLOps and DevOps engineers, technical product managers, and data scientists who want to build real-world multi-agent systems with secure, standardized context management. Familiarity with Python, LLMs, and basic system design is recommended.

Table of Contents:
Table of Contents

1. Introduction to Model Context Protocol
2. Building a Basic Agent with State and Deployment Flow
3. MCP for Non-Technical Readers Workflows
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. MCP and LangChain Integration
13. MCP and AutoGen Integration
14. MCP for Enterprise Knowledge Management
15. MCP for Personalization and Recommendation Systems
16. MCP for Multimodal Applications
17. Enterprise Knowledge Management
18. Case Studies and Applications
19. Ethical Considerations and Responsible AI with MCP
20. Advanced Topics and Future Directions

About the Author :
Professional Profile AI & Cloud Solutions Architect with 16+ years of success leading enterprise-grade digital transformation initiatives across Retail, Banking, Healthcare, and Manufacturing sectors. Well known for building scalable AI systems, integrating LLMs in production, and architecting secure, cloud-native applications. A Fellow of BCS and Senior IEEE Member, AI lead at Microsoft, open-source contributor, and frequent speaker at global technology forums and Podcasts. Key Achievements: Designed and deployed advanced Retrieval-Augmented Generation (RAG) and Voice RAG systems for Fortune 500 enterprises. Architected large-scale, context-aware agent systems using AutoGen, LangChain, and Model Context Protocol (MCP). Judge at Global NASA Space Apps, Microsoft Global Hackathon evaluating cutting-edge AI projects. Authored over 35 technical blogs, academic papers. Spoke on 20+ podcast in US, Australia, Europe, UK Core Technical Competencies: Enterprise AI & Multi-Agent System Architecture Microsoft Azure, GCP, AWS – Cloud-Native Design LLM Integration, RAG, LangChain, AutoGen MLOps, DevOps, CI/CD for AI Pipelines Distributed Systems, Microservices, Event-Driven Design AI Ethics, Governance, and Responsible AI Thought Leadership, Mentorship, and Public Speaking