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Full Description
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.
Contents
Table of Contents
Introduction to Model Context Protocol
Building a Basic Agent with State and Deployment Flow
MCP for Non-Technical Readers Workflows
MCP Components and Interfaces
MCP Architecture Overview
Server-Side Implementation
Client-Side Integration
MCP Security Model
MCP Performance Optimization
MCP and Multi-Agent Systems
MCP for Retrieval-Augmented Generation
MCP and LangChain Integration
MCP and AutoGen Integration
MCP for Enterprise Knowledge Management
MCP for Personalization and Recommendation Systems
MCP for Multimodal Applications
Enterprise Knowledge Management
Case Studies and Applications
Ethical Considerations and Responsible AI with MCP
Advanced Topics and Future Directions
