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Artículo: AMZ-B0FH51DXMG
Creating MCP Clients in Python with FastAPI: A Step-by-Step Handbook for Designing, Implementing, Testing, Securing, Scaling, and Deploying MCP-Powered AI Agent Interfaces with Python and FastAPI
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0.68 kg
0.68 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- Building AI-driven tools shouldn’t feel like wrestling with cobbled-together scripts and scattered services—this book shows you how to craft streamlined, production-ready MCP clients in Python with FastAPI, end to end.Book Summary From your very first MCP (Model Context Protocol) tool call to sophisticated multi-model orchestrations and real-world case studies, Creating MCP Clients in Python with FastAPI delivers a clear, hands-on roadmap. You’ll begin by setting up a rock-solid Python development environment, mastering FastAPI’s intuitive routing and Pydantic validation, and building your first /mcp/call endpoint. Step by step, you’ll layer in session management, asynchronous patterns, security best practices, and integration with databases, external APIs, and AI frameworks.Whether you’re coordinating ETL pipelines in finance, automating healthcare workflows under HIPAA, or running edge-AI on IoT devices, this book guides you through the patterns that matter: Docker-powered testing, JWT authentication, Redis-backed context stores, WebSocket updates, and serverless deployments. Along the way, real-world case studies—like an enterprise calendaring assistant that slashed scheduling tickets by 70%—bring each concept vividly to life.By the final chapters, you’ll be deploying your MCP clients to AWS ECS, GCP Cloud Run, and edge runtimes; orchestrating workflows via Airflow and event-driven triggers; and combining Hugging Face, LangChain, and custom vision or TTS models into unified, explainable pipelines. If you’re ready to graduate from proof-of-concept demos to reliable, scalable AI interfaces that users love, this book is your blueprint.What’s InsideFastAPI Fundamentals: Learn routes, Pydantic models, interactive Swagger docs, and best practices from day one.MCP Tool Modeling & Dispatch: Define JSON schemas, implement /mcp/call endpoints, and manage conversation state with Redis or databases.Security & Compliance: Implement OAuth2/JWT, input sanitization, CORS, HTTPS, rate limiting, audit trails, and field-level encryption.Scalability Patterns: Containerization with Docker, Kubernetes, AWS Fargate, serverless functions, and edge-deployments for ultra-low latency.Real-World Case Studies: Finance orchestration, healthcare pipelines, IoT edge-AI, and dynamic client extensions, complete with diagrams and code.Elevate your AI agent projects from fragile prototypes to robust, scalable services—grab your copy of Creating MCP Clients in Python with FastAPI today and start building production-grade AI interfaces that stand the test of real-world demands!
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