Nolan stark (57 Ergebnisse)

Paperback. Zustand: new. Paperback. Building software with a language model is fundamentally different from chatting with one through a consumer interface.Building AI Applications with the Claude API provides a practical introduction to integrating language intelligence into software, services, tools, and digital products.You will learn what an AI API actually does, how messages and roles work, how tokens and context windows affect application behavior, how to create and protect API credentials, and how to structure a project around an SDK or direct HTTP requests.The book then moves into the building blocks of useful AI applications. You will learn how to design system prompts, use examples, create tools, handle function calling, stream responses, request structured output, process documents and images, and combine multiple steps into a complete application feature.The final section addresses production engineering: AI-specific failure modes, validation, guardrails, model selection, token costs, latency, caching, scaling, security, privacy, and responsible deployment.For backend developers, full-stack developers, technical founders, software engineers, and builders integrating language models into applications, this book provides a practical bridge between API experimentation and production software.It focuses on the engineering decisions that matter after the first successful request-how to make the integration reliable, maintainable, cost-aware, secure, and useful to real users. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Taschenbuch. Zustand: Neu. Neuware - Building software with a language model is fundamentally different from chatting with one through a consumer interface.Building AI Applications with the Claude API provides a practical introduction to integrating language intelligence into software, services, tools, and digital products.You will learn what an AI API actually does, how messages and roles work, how tokens and context windows affect application behavior, how to create and protect API credentials, and how to structure a project around an SDK or direct HTTP requests.The book then moves into the building blocks of useful AI applications. You will learn how to design system prompts, use examples, create tools, handle function calling, stream responses, request structured output, process documents and images, and combine multiple steps into a complete application feature.The final section addresses production engineering: AI-specific failure modes, validation, guardrails, model selection, token costs, latency, caching, scaling, security, privacy, and responsible deployment.For backend developers, full-stack developers, technical founders, software engineers, and builders integrating language models into applications, this book provides a practical bridge between API experimentation and production software.It focuses on the engineering decisions that matter after the first successful request-how to make the integration reliable, maintainable, cost-aware, secure, and useful to real users. …

Taschenbuch. Zustand: Neu. Neuware - AI can generate code quickly. Becoming a better developer requires knowing what to build, how to verify it, and why the resulting code works.Becoming a Better Developer with Claude presents a practical way to use AI throughout the modern software development lifecycle without becoming dependent on generated answers.You will learn how to prepare a clean project, give an AI coding assistant the context it needs, write clearer instructions, plan complex changes, evaluate design choices, and break large tasks into small, reviewable steps.The book then moves into everyday engineering work: writing code, debugging, reproducing failures, tracing root causes, creating tests, reviewing changes, writing documentation, deploying software, and maintaining existing codebases.A major theme is verification. A plausible AI-generated answer is not the same thing as a verified engineering solution. You will learn how to turn vague symptoms into reproducible failures, connect fixes to evidence, add tests that prove behavior, and review generated changes before they become part of a real system.The goal is not to make you a faster copier of AI-generated code. It is to help you become a sharper developer with an AI coding partner that handles mechanical work while you retain engineering judgment.For programmers, software engineers, technical leads, and developers adopting AI-assisted coding, this book provides a practical framework for working faster without weakening the habits that make software reliable.…

Taschenbuch. Zustand: Neu. Neuware - Taking a generative AI application from a promising prototype to a reliable, enterprise-wide system takes a lot more than good prompting - it takes real architectural discipline. This book brings together advanced retrieval strategies, cost optimization, and behavioral control into one complete framework for context engineering done right.At its core, the book tackles the central challenge every serious AI deployment faces: feeding models the external, proprietary data they need without overwhelming their attention or blowing through your API budget. You'll get a full walkthrough of Anthropic's prompt caching system - structuring conversations so identical system prompts and tool definitions sit at the front, unlocking up to 90% cost savings and 85% faster response times on every call - along with strategies for managing cache state across long, multi-turn conversations.Beyond efficiency, there's a strong focus on accuracy. The book lays out a rigorous approach to Retrieval-Augmented Generation, covering contextual chunking, contextual embeddings, and semantic reranking to cut retrieval failures by nearly half and meaningfully reduce hallucinations caused by missing context. The throughline is treating context engineering as a genuine architectural discipline, not an afterthought - so your applications can reason accurately over massive datasets without falling apart at scale.This is essential reading for AI product managers, senior software engineers, and technical founders serious about scaling generative applications the right way.…

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Taschenbuch. Zustand: Neu. Neuware - Repetitive office work can quietly consume hours every week: copying information between systems, completing forms, sorting documents, researching websites, cleaning data, and processing the same requests again and again. The work may be simple, but its cumulative cost is not.Automating Repetitive Work With GPT-6 Astra is a practical guide to using AI to automate routine computer-based work while keeping people in control of decisions that matter. Rather than treating AI as a chatbot that only gives instructions, this book focuses on repeatable workflows where an AI agent can help carry out multi-step tasks with clear rules, checks, and human approval when necessary.You will learn how to identify strong automation candidates, measure the manual effort involved, prepare files and inputs, and turn an ordinary task into a reliable workflow. The book covers data entry, form filling, document processing, information extraction, web research, spreadsheets, batch processing, and routine administrative work.It also explains why good automation is not simply about making a process autonomous. Reliable automation requires boundaries, verification, exception handling, and a clear understanding of what happens when an input changes or something goes wrong. You will learn how to test workflows on realistic cases, inspect results, recover from failures, and improve successful automations over time.Through practical examples and a complete workflow project, Nolan Stark shows how business owners, operations teams, administrative professionals, analysts, freelancers, and non-programmers can approach AI automation without turning every problem into a custom software project.Inside, you will learn how to identify repetitive tasks worth automating, measure time and variation, prepare spreadsheets and documents, build data-entry workflows, extract structured information, automate recurring research, process batches of records, and add validation, exception handling, and human review.For readers searching for AI automation, workflow automation, data entry automation, document processing, business process automation, and browser task automation, this guide provides a practical framework for turning repetitive computer work into structured, testable, reusable workflows.The goal is simple: automate the repetition, preserve human judgment, and verify the result.…

Taschenbuch. Zustand: Neu. Neuware - AI becomes far more useful when it is treated as a working partner rather than a machine that simply answers questions.Mastering Claude AI for Professional Work is a practical, non-technical guide to using Claude for thinking, learning, writing, research, problem-solving, decision-making, and everyday professional work.You will begin with a clear understanding of what Claude is, what it does well, where it can fail, and how to communicate with it effectively. The book teaches you how to provide context, break large requests into smaller pieces, iterate on answers, and treat the first response as a draft rather than an unquestioned result.The book then shows how to use Claude as a reasoning partner. You will learn how to stress-test ideas, examine alternative perspectives, identify blind spots, learn new concepts, create better content, summarize information without losing meaning, work with data, organize research, and build useful documents and reports.The later chapters focus on real professional problem-solving. Learn how to identify the underlying problem, compare options, pressure-test decisions, manage uncertainty, avoid overconfidence, and develop daily and weekly habits that make AI a natural part of your work.This book does not require programming knowledge. It is designed for professionals who want to use Claude effectively while keeping their own judgment, accountability, and voice.For managers, analysts, consultants, educators, researchers, writers, entrepreneurs, and other knowledge workers, this is a practical guide to making AI a useful part of everyday professional thinking and work. …

Taschenbuch. Zustand: Neu. Neuware - The Model Context Protocol (MCP) is changing how AI applications connect to tools, data, services, and external systems. But understanding MCP in theory is very different from building an MCP server that is secure, testable, reliable, and ready for production.Professional MCP Development with Claude AI is a practical guide to building MCP servers, MCP clients, AI tools, resources, prompts, and enterprise integrations for modern AI applications.Starting with the foundations of the Model Context Protocol, you will learn the MCP client-host-server architecture, tools, resources, prompts, transports, capabilities, sessions, JSON-RPC communication, versioning, roots, and elicitation. The book explains how these pieces fit together and why a standardized protocol can replace large amounts of custom AI integration code.You will then build a production-ready MCP server from scratch. Learn how to structure an MCP project, work with the official Python and TypeScript SDKs, validate inputs, handle configuration and secrets, use the MCP Inspector, document your server, manage versioning, handle external API failures, and decide when one MCP server should become several.The book also explores MCP tools and tool calling, including tool descriptions, schemas, return values, idempotency, safe retries, REST API integrations, resources, reusable prompts, large results, subscriptions, prompt arguments, and robust MCP clients.The production chapters address MCP security, authentication, authorization, prompt injection, least privilege, supply-chain risk, secrets management, auditing, compliance logging, data minimization, human approval, testing, observability, deployment, scaling, cost control, and incident response.Whether you are learning MCP for the first time or preparing to deploy MCP servers across an enterprise, this book gives you a practical path from understanding the protocol to building reliable integrations that AI applications can actually use.…

Taschenbuch. Zustand: Neu. Neuware - AI assistants answer questions. Autonomous AI agents are designed to pursue goals, make decisions, use tools, observe results, and continue working across multiple steps.Building Autonomous AI Agents with Claude AI is a practical guide to designing, building, evaluating, and operating AI agents that can perform complex tasks with controlled autonomy.You will begin by learning what actually makes a system an AI agent, how agents differ from chatbots and ordinary API calls, and when an agent is genuinely necessary. The book introduces the autonomy spectrum and shows how to choose an appropriate level of independence based on risk, reversibility, and the cost of human review.You will then learn the reason-act-observe loop behind autonomous agents. The book explains how agents decide what to do, call tools, receive results, continue or stop, and handle failures. You will learn how to design purpose-built tools, create reliable schemas, use the Model Context Protocol, and test tools before allowing an agent to operate with them.The next stage covers AI agent orchestration and complex multi-step tasks. Learn how to break goals into executable steps, delegate work to sub-agents, choose between plan-then-execute and interleaved planning, manage persistent state, distinguish memory from context, prevent state drift, resume long-running tasks, and handle failures without creating worse outcomes.The book then focuses on AI agent safety and production engineering: permissions, least privilege, approval gates, sandboxing, human-in-the-loop controls, audit logs, evaluation datasets, regression testing, human baselines, observability, cost management, latency, incremental deployment, model upgrades, and behavioral drift.This book is written for developers, AI engineers, software architects, technical founders, and builders who want to move beyond simple prompts and create autonomous AI agents capable of performing real multi-step work.The goal is not maximum autonomy.The goal is useful autonomy that can be understood, evaluated, controlled, and trusted. …

Taschenbuch. Zustand: Neu. Neuware - AI can accelerate software development, but faster code generation creates a greater need for disciplined testing. A feature can look complete while important edge cases, business rules, integrations, or failure paths remain untested. A passing test suite is useful evidence, but it does not automatically prove that the right behavior was tested.Testing and Regression Detection With GPT-6 Astra is a practical guide to building strong verification into AI-assisted software development. It covers the complete testing loop: defining expected behavior, creating tests, running test suites, interpreting failures, diagnosing defects, detecting regressions, evaluating coverage, and keeping human review involved.You will learn how to translate requirements into testable behaviors, choose between unit, integration, and end-to-end testing, create edge-case and negative tests, generate useful tests with AI, diagnose failures, and add regression protection after a defect has been fixed.A central theme is that more tests do not automatically mean better tests. AI can produce tests that look reasonable but simply mirror the implementation. Weak requirements can create weak tests, and a green build can still hide untested behavior. The book therefore shows how to challenge assumptions and evaluate what a test actually proves.Inside, you will learn how to build a verification-first workflow, define acceptance conditions, use unit and integration testing effectively, create edge-case tests, generate tests with AI, interpret test results, diagnose failures, design regression protection, evaluate coverage and confidence, review AI-assisted verification, and build human escalation into automated testing.This book is written for software developers, QA engineers, test engineers, technical leads, and teams adopting AI coding agents. It applies to web applications, APIs, services, libraries, databases, and other systems where rapid development must be balanced with reliable quality assurance.For readers searching for AI software testing, automated software testing, regression testing, test automation, unit testing, integration testing, and QA automation, this guide provides a practical framework for making verification keep pace with AI-assisted development.The objective is not to eliminate testing. It is to make verification deliberate, repeatable, and closely connected to the behavior the software is expected to deliver.…

Taschenbuch. Zustand: Neu. Neuware - Good pair programming is a conversation: clarify the requirement, inspect the context, plan an approach, implement a change, review it, run checks, question assumptions, and improve the result together. Working effectively with an AI coding agent requires the same discipline, even when the AI can read files and write code at high speed.Pair Programming With GPT-6 Astra is a practical guide to using an AI coding agent as an active engineering partner for planning, implementation, explanation, code review, refactoring, and iterative problem solving. It is not a collection of prompts for copying code from a chat window. It is a workflow for working inside real repositories with real requirements, tests, and constraints.You will learn how to give an agent useful context, establish coding standards, define acceptance criteria, plan before implementation, review generated code, ask for explanations, refactor collaboratively, handle disagreements, and preserve useful context across multiple iterations.A complete developer-and-Astra session demonstrates how a natural-language task can become a plan, a code change, verification steps, and a reviewable result.The book keeps human judgment at the center. An AI agent can produce technically valid code that violates project conventions, misunderstands the requirement, creates unnecessary abstraction, or increases maintenance cost. The goal is not to remove the developer from the process, but to use AI where it can increase speed without weakening engineering quality.Inside, you will learn how to build an AI pair-programming workflow, provide repository context, establish coding standards, plan implementation, review AI-generated code, ask for explanations, refactor collaboratively, handle disagreements, preserve context, and build repeatable AI-assisted development routines.This book is designed for software developers, programmers, technical leads, engineering teams, and professionals experimenting with AI coding assistants.For readers searching for AI pair programming, AI coding assistants, code review with AI, collaborative coding, AI refactoring, developer workflow, and software development collaboration, this guide turns those concepts into a practical operating routine.The central habit is simple: do not ask the agent merely to produce code. Ask it to work with you, give it context, establish constraints, review its work, run real tests, and iterate until the result is both useful and understood.…

Taschenbuch. Zustand: Neu. Neuware - AI-assisted coding is moving beyond autocomplete and isolated code snippets. A modern coding agent can work across an entire repository: understanding project structure, reading dependencies, planning changes, editing multiple files, running commands, executing tests, debugging failures, and reviewing results.GPT-6 Astra for Coding and Software Engineering is a practical guide to this broader model of AI-assisted software development. It focuses on repository-level engineering rather than one-shot code generation, showing how to use an AI agent inside a real development environment while keeping requirements, testing, source control, and human review at the center.You will learn how to give an agent useful context, inspect a project before changing it, turn requirements into implementation plans, work across multiple files, use terminal tools, run tests and linters, diagnose failures, refactor existing code, and review changes before they become part of the system.A complete repository-level project brings the workflow together, from natural-language requirements to planning, implementation, verification, and review.The book also addresses common failure modes. AI can generate plausible code that misunderstands a requirement, violates project conventions, introduces unnecessary complexity, or solves one problem while creating another. A strong workflow therefore treats AI output as work to inspect and verify, not as unquestionable authority.Inside, you will learn how to work with repository-level AI coding agents, provide project context and constraints, inspect dependencies, turn requirements into plans, make multi-file changes, use terminal tools, run tests and builds, diagnose failures, review code, refactor responsibly, manage longer agentic tasks, and integrate Git and human review.This book is for software developers, programmers, technical leads, architects, engineering teams, and professionals exploring AI-native development.For readers searching for AI coding agents, AI software development, repository-level development, developer productivity, automated coding workflows, code generation tools, and agentic software engineering, this guide provides a practical framework for building with AI while maintaining sound engineering discipline.The workflow is simple: understand the repository, define the task, plan before changing, execute with tools, verify the result, review the diff, and keep a human accountable for important decisions.…

Taschenbuch. Zustand: Neu. Neuware - AI-assisted automation is moving beyond traditional scripts. A modern computer-using agent can work with browsers, desktop applications, forms, spreadsheets, CRMs, and other software interfaces while following a structured process of perception, decision, action, and verification.GPT-6 Astra for Computer Use and AI Automation is a practical guide to designing these AI-powered workflows. It explores how computer-using AI can help automate multi-step tasks while maintaining security boundaries, verification, exception handling, and human approval when necessary.The book begins with the foundations of computer use and explains how AI agents differ from traditional automation and robotic process automation. It then moves into browser workflows, desktop applications, screen understanding, clicking and typing, navigation, form completion, spreadsheets, business systems, changing interfaces, permissions, recovery, and verification.The goal is not blind autonomy. It is dependable computer automation. That means defining the task before execution, controlling access, understanding what information is being handled, verifying results after important actions, and requiring human approval when a mistake could have serious consequences.Through practical examples, you will learn how to break complex work into smaller stages, provide useful context, respond to changing interfaces, and recover when an expected result does not appear.Inside, you will learn how to understand computer-use workflows, compare AI computer use with traditional automation, design browser automation, work with desktop applications, automate forms and navigation, handle spreadsheets and CRMs, manage interface changes, control permissions, build verification steps, add human checkpoints, and design multi-step automation workflows.The book is intended for operations professionals, business owners, analysts, automation builders, administrative teams, developers, and technical professionals interested in AI automation beyond chat.For readers searching for AI computer use, browser automation, desktop automation, workflow automation, robotic process automation, CRM automation, and business process automation, this guide provides a practical framework for building workflows that are useful, testable, and controlled.The objective is straightforward: automate repetitive computer work without making the underlying process harder to trust. …

Taschenbuch. Zustand: Neu. Neuware - An AI application becomes significantly more powerful when it can access live information and perform controlled actions-but that power also creates new engineering responsibilities.Connecting Claude AI to External Tools focuses on the practical problem of giving AI applications access to real systems without giving the model uncontrolled power over them.You will learn how to define tools, design structured schemas, implement the tool-use loop, connect REST APIs, protect credentials, and shape tool results so an AI agent can use them reliably.The book gives special attention to database access, explaining why free-form query generation is dangerous and how parameterized operations, least-privilege permissions, read/write boundaries, result limits and narrow data exposure create safer integrations.You will also learn how the Model Context Protocol fits into modern tool connectivity, and how to design multi-tool agents that can coordinate several capabilities without becoming unpredictable.The final chapters deal with real-world action: retries, idempotency, write operations, side effects, confirmation flows, human checkpoints, escalation, agent handoffs, monitoring and production deployment.For backend developers, AI engineers, software architects and DevOps professionals, this book is about the final step beyond conversation: building AI systems that can interact with real software and data while keeping every consequential action controlled, testable and accountable.…

Taschenbuch. Zustand: Neu. Neuware - A general AI assistant can answer questions. A specialized AI agent is built to do a specific job.Creating Specialized AI Agents with Claude explains how to turn a general-purpose AI model into a focused digital worker with the knowledge, procedures, tools, and domain expertise required to perform a defined role reliably.You will learn how to define an agent's actual job before writing its instructions, organize reusable knowledge, create knowledge documents, package skills, and turn repeated tasks into clear procedures. The book then moves into tool use and function calling, showing how an agent can interact with inventory systems, databases, calendars, APIs, and other external services.Special attention is given to safe execution. You will learn how to distinguish read actions from consequential changes, introduce confirmations and human checkpoints, handle failed tools, apply permissions, and create guardrails before giving an agent real responsibility.The book also explains how to teach domain expertise through worked examples rather than simply adding more facts. You will learn how to capture exceptions, build example banks, test an agent's judgment, turn real failures into lasting improvements, and recognize when an agent is actually ready to expand its role.The final chapters address deployment, maintenance, gradual rollout, long-term evaluation, and responsible growth.For AI developers, automation builders, technical founders, operations professionals, and anyone creating specialized AI agents, this guide provides a practical framework for building agents that do a defined job instead of merely holding a conversation.…

Taschenbuch. Zustand: Neu. Neuware - Vibe coding makes it possible to describe software in plain language and watch AI generate working code. The difficult part begins after that first exciting result.Mastering Vibe Coding with Claude AI teaches you how to turn fast AI-generated code into software you can actually understand, test, secure, maintain, deploy, and trust.You will learn what vibe coding really means, how Claude generates code from natural-language instructions, how to create a safe starting point for experimentation, and why vague requests often lead to unpredictable results.The book then moves from rapid generation to verified code. You will learn how to write clearer requests, define goals and scope, read code you did not write, spot subtle mistakes, create meaningful automated tests, reproduce failures, and debug problems using evidence rather than guesses.The later chapters focus on making AI-generated software last. You will learn how to refactor quickly produced code without breaking it, understand unfamiliar codebases, protect secrets, improve performance, prepare software for real users, monitor applications after release, and create a sustainable AI-assisted development workflow.The central lesson is simple: speed creates the first draft; testing, review, security, refactoring, deployment, and monitoring create software that lasts.For developers, indie hackers, technical founders, programmers, and builders using AI to create software, this book provides a practical framework for moving from fast prototypes to reliable applications.…

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - Building a useful AI application requires more than getting a language model to produce an impressive response. A dependable system needs clear instructions, controlled context, reliable tool use, useful retrieval, testing, guardrails, and production monitoring.Building Intelligent Applications with Claude AI provides a practical path from the first API request to production-ready AI software.You will learn how to design system prompts that produce consistent behavior, manage multi-turn conversations, work with growing context, and handle long-running sessions. The book then moves into tool use, multi-tool assistants, document retrieval, grounded responses, source citation, and the architecture behind modern AI agents.You will also learn how to design agentic workflows, coordinate multiple agents, create guardrails, introduce human checkpoints, handle escalation, and decide when autonomy is appropriate.The final chapters focus on the engineering work that determines whether an AI feature survives real users: testing non-deterministic behavior, separating infrastructure failures from reasoning failures, handling rate limits, reducing latency, managing token costs, monitoring quality, and preparing applications to scale.Designed for developers, technical founders, product engineers, AI builders, and software architects, this book focuses on practical AI application development rather than theoretical machine learning.From assistants and automation tools to retrieval systems and multi-step agents, the goal is to help you build AI software that is useful, testable, controlled, and ready for real-world use.…

Taschenbuch. Zustand: Neu. Neuware - A language model becomes much more useful when it can reach beyond the information contained in its immediate context.Building Connected Systems with Claude AI explains how to create applications that connect AI to external APIs, databases, structured data sources, tools, and other software systems.You will learn how tool definitions work, how schemas constrain model inputs, how the tool-use loop operates, and how to connect AI applications to external services safely.The database chapters focus on a critical engineering boundary: the model should choose among approved operations rather than freely author queries. You will learn about parameterized queries, least-privilege access, result limits, read/write boundaries, and structured tool results.A major section covers the Model Context Protocol (MCP) and explains the role of servers, tools, resources, clients, and standardized connections.The final chapters address real-world reliability: guardrails, failure handling, testing, multi-agent handoffs, escalation, monitoring, logging and staged production deployment.For backend engineers, AI developers, software architects, and DevOps professionals, this book provides a practical framework for creating connected AI systems that can access real information and services while remaining controlled and reviewable.…

Taschenbuch. Zustand: Neu. Neuware - The next stage of AI automation is not simply adding more tasks to one assistant. It is creating specialized AI agents with clearly defined jobs, controlled tools, measurable responsibilities, and appropriate human oversight.Building an AI Workforce with Claude explains how to design that kind of system.You will learn how to define an AI agent, write durable instructions, create guardrails, provide tools, implement function calling, and move work between specialized agents.The book then applies those principles to real organizational functions. You will build conceptual designs for coding agents, QA and testing agents, DevOps and release agents, research analysts, competitive intelligence agents, document and knowledge-base agents, operations coordinators, customer support agents, marketing agents, and sales and CRM agents.The later chapters address multi-agent orchestration and governance. You will learn when multiple agents are justified, how tasks should be routed, how handoffs should work, where humans need to approve decisions, and how audit logs and review processes can keep an AI workforce accountable.For developers, technical leaders, founders, enterprise architects, automation builders, and organizations exploring AI agents, this book provides a practical framework for turning autonomous AI from an abstract concept into a set of defined digital workers.…

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Paperback. Zustand: new. Paperback. The Model Context Protocol (MCP) is changing how AI applications connect to tools, data, services, and external systems. But understanding MCP in theory is very different from building an MCP server that is secure, testable, reliable, and ready for production.Professional MCP Development with Claude AI is a practical guide to building MCP servers, MCP clients, AI tools, resources, prompts, and enterprise integrations for modern AI applications.Starting with the foundations of the Model Context Protocol, you will learn the MCP client-host-server architecture, tools, resources, prompts, transports, capabilities, sessions, JSON-RPC communication, versioning, roots, and elicitation. The book explains how these pieces fit together and why a standardized protocol can replace large amounts of custom AI integration code.You will then build a production-ready MCP server from scratch. Learn how to structure an MCP project, work with the official Python and TypeScript SDKs, validate inputs, handle configuration and secrets, use the MCP Inspector, document your server, manage versioning, handle external API failures, and decide when one MCP server should become several.The book also explores MCP tools and tool calling, including tool descriptions, schemas, return values, idempotency, safe retries, REST API integrations, resources, reusable prompts, large results, subscriptions, prompt arguments, and robust MCP clients.The production chapters address MCP security, authentication, authorization, prompt injection, least privilege, supply-chain risk, secrets management, auditing, compliance logging, data minimization, human approval, testing, observability, deployment, scaling, cost control, and incident response.Whether you are learning MCP for the first time or preparing to deploy MCP servers across an enterprise, this book gives you a practical path from understanding the protocol to building reliable integrations that AI applications can actually use. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.