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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

4 (1)
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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14
Other Books You May Enjoy
15
Index

Packaging and distribution

Before you even type a single line of code, you need to consider what you’re building. With MCP, you have different options on how to package and distribute your application.

Packaging options

Let’s look at some packaging options.

  • Standalone server: This is designed for public access or for private use. Regardless of private or public access, you need to think about authentication, authorization, and data privacy. But for private distribution within a company or organization, you need to think about discoverability, how internal teams will find and use the service, and also compliance with internal policies.
  • Embedded client/server: Such a system probably consists of a client and a server component, and the client most likely ships with AI capabilities, so make sure you consider the implications of that, such as data privacy and security and responsible AI use, and any regulatory requirements that may apply.
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Learn Model Context Protocol with Python
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