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Learn Model Context Protocol with Python

Learn Model Context Protocol with Python

By : Christoffer Noring
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Learn Model Context Protocol with Python

Learn Model Context Protocol with Python

By: Christoffer Noring

Overview of this book

Learn Model Context Protocol with Python introduces developers, architects, and AI practitioners to the transformative capabilities of Model Context Protocol (MCP), an emerging protocol designed to standardize, distribute, and scale AI-driven applications. Through the lens of a practical project, the book tackles the modern challenges of resource management, client-server interaction, and deployment at scale. Drawing from Christoffer's expertise as a published author and tutor at the University of Oxford, you’ll explore the components of MCP and how they streamline server and client development. Next, you’ll progress from building robust backends and integrating LLMs into intelligent clients to interacting with servers via tools such as Claude for desktop and Visual Studio Code agents. The chapters help you understand how to describe the capabilities of hosts, clients, and servers, facilitating better interoperability, easier integration, and clearer communication between different components. The book also covers security best practices and building for the cloud, ensuring that you're ready to deploy your MCP-based apps. Each chapter enables you to develop hands-on skills for building and operating MCP-based agentic apps. The Python primer at the end rounds out the practical toolkit, making this book essential for any team building AI-native applications today.
Table of Contents (17 chapters)
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Index

Introduction to the Model Context Protocol

Generative AI has rapidly become a force in today’s technological landscape, reshaping industries and redefining how we approach problem-solving. From natural language processing to image generation, the integration of generative AI into various domains has opened up new possibilities for innovation and efficiency.

For us developers, integrating generative AI into app development workflows is not without its complexities. We must carefully evaluate factors such as model accuracy, ethical considerations, and computational efficiency.

It’s in the process of building applications that we need to consider how we standardize the way we build our AI applications. Standardization means that everything looks the same, which should mean easier integration and collaboration across different teams and tools.

This is where the Model Context Protocol (MCP) comes in, to standardize how we ensure that AI-powered applications can easily find what they need from tools, content, and prompts; more on that shortly.

The chapter covers the following topics:

  • How we got here, from SOAP to REST to GraphQL to gRPC to MCP
  • The need for a standard
  • Endless possibilities: know how to prompt, and a world of MCP servers is your oyster
  • What is the MCP?
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