Model Context Protocol (MCP) : The Developer Guide
Language: English
Published by Meridian Certification Press, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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nach der Bestellung gedruckt Neuware - Printed after ordering - Independent study guide for the Model Context Protocol (MCP): The Developer Guide certification examination (N/A (technical guide)). Includes 280+ pages of study material and 150 practice questions with detailed answer explanations.
Seller Inventory # 9781608887842
- Title
- Model Context Protocol (MCP) : The Developer Guide
- Author
- Meridian Certification Press
- Publisher
- Meridian Certification Press
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1608887847
- ISBN 13
- 9781608887842
- Item weight
- 898 grams
- Dimensions
- 280x216x16 mm
Model Context Protocol (MCP): The Developer Guide is an authoritative 300-page technical reference for software engineers, AI application developers, and enterprise architects building systems that connect large language models to external tools, data sources, and services through the Model Context Protocol. MCP is the open standard, originated by Anthropic and now broadly adopted, that solves the "thousand integrations" problem by providing a universal protocol for AI tool connectivity. This guide covers the complete MCP specification, implementation patterns in TypeScript and Python, security best practices, and deployment strategies. It is designed for developers building or integrating MCP servers and clients, as well as architects evaluating MCP for enterprise AI infrastructure. This is an independent technical reference not affiliated with Anthropic or any other technology company.
The guide is organized into three parts plus extensive reference material. The opening chapter traces AI tool integration from prompt engineering in 2020-2022 through function calling in 2023 and framework abstraction in 2023-2024 to protocol standardization beginning in 2024. Part I: Core Concepts covers MCP's three-layer architecture of hosts, clients, and servers, the JSON-RPC 2.0 protocol layer, transport mechanisms including stdio, legacy SSE, and Streamable HTTP, and the connection lifecycle from initialization through capability negotiation to shutdown. Dedicated chapters cover the three core primitives: Resources for URI-based data exposure with subscriptions, Tools for callable functions with schema validation, and Prompts for templated interaction patterns. A chapter on Sampling and Elicitation explains server-initiated LLM completions. Part II: Building MCP Servers provides quickstart guides for TypeScript and Python, followed by chapters on implementing resources and tools in depth, error handling and logging, and testing including unit, integration, and protocol conformance validation. Part III: Advanced Patterns covers multi-server architectures, security best practices including authentication, authorization, and secrets management, performance optimization, and deployment patterns for local, cloud, and hybrid environments.
The reference section includes a protocol specification summary, JSON-RPC message format reference, error codes reference, a curated catalog of notable MCP servers, a quick reference card, and a glossary. Extended appendices provide complete working examples with source code, a migration guide from proprietary tool integrations to MCP, a troubleshooting guide organized by symptom, a guide to building MCP clients, an MCP Design Patterns Catalog, a FAQ, and the MCP ecosystem roadmap covering upcoming features and community developments. All technical content has been adversarially verified against the current MCP specification.
The Model Context Protocol is rapidly becoming the standard interface between AI applications and the tools they use, with adoption accelerating across the industry. Developers who understand MCP are positioned at the center of the AI infrastructure stack, able to build the connective tissue that makes AI systems useful in production. Whether you are building MCP servers to expose your data and services to AI, integrating MCP clients into applications, or architecting enterprise AI platforms, this guide provides the comprehensive technical foundation needed for robust, secure, and performant MCP implementations.
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