{ "item_title" : "Python MCP Server Engineering", "item_author" : [" Alistair Veyne "], "item_description" : "The Model Context Protocol makes it possible for AI agents and LLM applications to interact with external tools, data, and services. But building a server that works in a demo is very different from building one that can survive real users, concurrent workloads, authentication requirements, failures, and production deployments.Python MCP Server Engineering takes you through that entire journey.Rather than presenting MCP as a collection of isolated examples, this book follows one realistic project-TicketFlow MCP, a support-ticket and knowledge-base server-and progressively develops it from an initial Python scaffold into a secure, observable, containerized production system.You will learn how to: Understand MCP's architecture, lifecycle, tools, resources, prompts, and schemasStructure Python MCP projects for maintainability and long-term growthBuild MCP servers with Python, async I/O, and modern dependency managementDesign tools that models can discover and use reliablyExpose resources for data, files, documentation, and contextual informationCreate effective server-side promptsImplement stdio and Streamable HTTP transportsHandle streaming, concurrency, latency, partial results, and long-running operationsSecure MCP servers with OAuth 2.1, API keys, tokens, and scope-based authorizationApply least-privilege security to individual capabilitiesTest tools, schemas, sessions, authorization, and failure conditionsDebug MCP clients and servers with inspection and structured diagnosticsAdd structured logging, OpenTelemetry tracing, and Prometheus metricsDockerize and deploy MCP servers to production environmentsDesign for reliability, fault tolerance, graceful shutdown, and scalingManage backward compatibility, deprecation, versioning, and migrationsIntegrate MCP servers with RAG pipelines, autonomous agents, and internal platformsBuild a production reference architecture that can be adapted to other domainsThe emphasis throughout is on engineering decisions that hold up outside a tutorial environment: strict schemas at the boundary, domain logic independent of MCP, centralized authorization, shared infrastructure, observable request flows, controlled concurrency, and deliberate upgrade strategies.You do not need prior MCP experience. However, you should be comfortable with Python, type hints, async/await, and basic HTTP services.Whether you are a backend developer moving into AI infrastructure, a Python engineer building agent-facing services, or a platform engineer exposing internal systems to AI, this book gives you a practical path from first MCP server to production-ready system.Build the server. Secure it. Test it. Observe it. Deploy it. Then build something real.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/819/272/9798192721728_b.jpg", "price_data" : { "retail_price" : "35.00", "online_price" : "35.00", "our_price" : "35.00", "club_price" : "35.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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