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Home > Computing and Information Technology > Computer science > Systems analysis and design > The MCP Tool Calling Playbook: Blueprints for Structuring Agent Behaviors with Context, Modularity, and API Control
The MCP Tool Calling Playbook: Blueprints for Structuring Agent Behaviors with Context, Modularity, and API Control

The MCP Tool Calling Playbook: Blueprints for Structuring Agent Behaviors with Context, Modularity, and API Control


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About the Book

In the new age of intelligent agents and API-native LLMs, tool-calling isn't optional-it's the backbone of serious AI engineering. This book is your comprehensive blueprint for designing agents that reason, act, and adapt using the Modular, Contextual, Prompt-driven (MCP) method. Whether you're orchestrating web scraping pipelines, chaining LangChain tools, or deploying scalable multi-step agents, this playbook gives you the working knowledge and battle-tested patterns to build real-world, production-ready systems. Written by an AI systems expert and trusted voice in applied LLM architecture, this guide strips away the hype and focuses on what actually works. It is grounded in tested code, real-world deployment scenarios, and practical wisdom gained from building agent-based systems that are used at scale. About the Technology: Tool-calling agents powered by large language models represent a significant shift from passive text generation to context-aware, action-taking systems. With libraries like LangChain, CrewAI, and OpenAI Function Calling, developers now have access to tools that allow LLMs to query APIs, interact with databases, invoke workflows, and make decisions in modular pipelines. The MCP pattern formalizes this shift into a practical framework for agent development. What's Inside: A complete breakdown of the MCP architecture with real-world examples. How to define, rank, and route tools dynamically using LLMs or heuristics. Techniques for passing context, chaining tools, and handling multi-step workflows. Working code examples using Python, LangChain, FastAPI, and Docker. Tool invocation, validation, response parsing, and fallback design strategies. Production-grade deployment practices with CI/CD, observability, and scaling tips. Security, rate-limiting, and ethical considerations for AI agents in real environments. Who This Book Is For: If you're a developer, ML engineer, software architect, or technical product lead building LLM agents that need to interact with APIs, tools, or external systems, this book is for you. It is suitable for both beginners and advanced practitioners who want to structure agent behavior with modular control and system-level thinking. The AI world is moving fast-and the gap between those who understand agentic design and those who don't is growing wider by the day. The earlier you master tool-calling architectures, the better positioned you'll be to build scalable, intelligent applications in a landscape that's becoming more API-driven and agent-oriented. Packed with complete working code, tested patterns, architectural breakdowns, and advanced deployment strategies, this book delivers more than theory-it delivers working systems. You're not just buying a book. You're investing in your ability to build the next generation of intelligent software. Whether you're building your first LLM-powered toolchain or scaling a fleet of multi-agent systems-this is the playbook you've been missing. Grab your copy now and start building modular, context-aware, tool-calling agents that actually get the job done.


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Product Details
  • ISBN-13: 9798299265736
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 244 mm
  • No of Pages: 318
  • Returnable: N
  • Sub Title: Blueprints for Structuring Agent Behaviors with Context, Modularity, and API Control
  • Width: 170 mm
  • ISBN-10: 8299265738
  • Publisher Date: 22 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 17 mm
  • Weight: 562 gr


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The MCP Tool Calling Playbook: Blueprints for Structuring Agent Behaviors with Context, Modularity, and API Control
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