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About the Book

Standardization and standards are essential for any technology adoption at scale. Standards drive interoperability, enabling consistency in how a product, process, or technology should be designed, built, or used. This is achieved by developing a set of rules, guidelines, or specifications that one must adhere to. Without standards, the simple, seamless experience we take for granted would be replaced by constant frustration, chaos, and inefficiency. Imagine a world without TCP/IP, HTTP, and HTML that power the Internet today. Standards are woven into the fabric of our lives, often in ways we don't even notice. Ever noticed how you can work seamlessly on your laptop, a PC, and a phone without worrying about the keyboard layout? The QWERTY keyboards are the de facto standard. For technologies that empower humans and drive human progress, standardization is not just a wish but a critical requirement. Artificial Intelligence (AI) is one such technology that is changing how we work. 

The evolution of Large Language Models (LLMs) is enabling a wide range of capabilities across different industries and reducing the barrier to entry to develop AI applications. For creating AI applications, the OpenAI API format, for LLM interactions, became a de facto standard, making it easy to switch between LLM providers without worrying about constant code changes. This evolution from being just text completion machines to reasoning and thinking machines is accelerating the adoption of AI and has given rise to what we now call agentic AI. These AI agents, with an LLM as the brain, can observe and act autonomously to achieve a given goal. This requires access to external and real-time information. LLM providers have enabled techniques such as Retrieval Augmented Generation (RAG) and tool calling to provide the necessary context. This support, while it exists across different providers, is highly fragmented, with each provider choosing disparate ways of implementation, resulting in complex LLM and tool integrations and vendor lock-in. This is a big challenge for organizations and individuals hoping to 

To address this challenge, the brilliant people at Anthropic came up with a standard specification called the Model Context Protocol (MCP). MCP is an open standard and framework that standardizes how AI applications and agents provide context to LLMs. With many hyperscalers, enterprises, and developers rallying behind MCP, it has truly become an essential part of developing powerful AI applications and agents. However, adopting a standard that has not yet reached maturity has its pros and cons. While you get to review, use, and contribute to an evolving standard, the challenges around quality and security constrain adoption. 

This book provides you with the fundamentals you need to start using and building MCP support into your products. Security is a big aspect of building agentic AI systems, and you will learn about the current challenges and how to work around them. The MCP ecosystem is growing, and this book equips you with the knowledge to select the right tool you need for your AI application or agent. Tool and data access is a critical part of building agentic AI systems, and this book helps you master the art and science of providing the right context to the LLMs.

About the Author

Ravikanth Chaganti

@ravikanth

Ravikanth is a Distinguished Engineer and an architect in the AI Solutions Engineering team at Dell Technologies. He is a multi-year recipient of the Microsoft Most Valuable Professional (MVP) award in Azure AI Platform. Ravikanth is the author of Windows PowerShell Desired State Configuration Revealed (Apress) and Pro PowerShell Desired State Configuration (Apress). He self-published several books on Leanpub. He can be seen speaking regularly at local user group events and conferences in India and abroad.