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 Description
AI 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 for
AI/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.
