Name: AI Agents with MCP: Model Context Protocol for Building Clients, Servers, and End-to-End Agents
Author: Kyle Stratis (Author)
ISBN: 9798341639553

AI Agents with MCP: Model Context Protocol for Building Clients, Servers, and End-to-End Agents book cover

AI Agents with MCP: Model Context Protocol for Building Clients, Servers, and End-to-End Agents

Author(s): Kyle Stratis (Author)

Publisher: O’Reilly Media
Publication Date: October 20, 2026
Edition: 1st
Language: English
Print length: 347 pages
ASIN: B0FYCBVGX7
ISBN-13: 9798341639553

Book Description

Building context-aware AI agents requires connecting models to a fragmented landscape of tools, databases, and APIs. Using the latest version of Model Context Protocol (MCP) and Python SDK (v2.x), AI Agents with MCP shows you how to address that challenge to build clean, standardized, and production-ready systems. Whether you’re developing complex agentic workflows, bridging cross-platform tools, or creating robust multi-agent systems, this book takes you through every layer of MCP, from protocol structure to server and client implementation.

Author Kyle Stratis provides the practical expertise needed to build fully functional MCP servers, clients, and more, and gives you a deep systems-level understanding of MCP’s capabilities and limitations. With its flexible, model-agnostic design, MCP continues to gain traction across the generative AI community; this book ensures you’re ready to build with it confidently and effectively.

Understand the structure and core concepts of the Model Context Protocol
Focus on feature-completeness in treatment of clients and servers
Consume tools, prompts, and data via MCP-based agent workflows
Extend agent capabilities with MCP for large-scale and AI-native systems

Editorial Reviews

About the Author

Kyle Stratis is the founder and principal consultant of Stratis Data Labs, where he provides startups and large firms alike with his expertise in AI engineering, computer vision, data engineering, and machine learning engineering garnered over a decade as a software engineer. He also serves as the Vice President of Engineering at the Sequel Institute, where he brings cutting edge AI research into production. In addition to designing data architecture, building AI platforms and agents, and migrating analytics workflows to the cloud for his clients, he writes about Python, LLMs, and personal knowledge management.

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