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Artículo: AMZ-B0G4T18DXW
Model Context Protocol in Production: Implementing Secure, Scalable Connectors for LLMs using Python, TypeScript, and JSON-RPC.
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Kindle
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0.84 kg
0.84 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- The "Hello World" era of AI is over. It’s time to build the infrastructure. We have moved past the novelty of chatting with Large Language Models. The next frontier of AI is not about better prompts, it is about better context. It is about connecting GPT-4, Claude, and Llama to the messy, real-world data that powers your business: your production databases, your Kubernetes clusters, your internal APIs, and your local file systems. But bridging the gap between a probabilistic AI and a deterministic system is fraught with peril. How do you give an LLM access to your SQL database without letting it delete your users? How do you feed it 10,000 log lines without bankrupting your token budget? How do you scale an agent from a laptop prototype to an enterprise microservice handling thousands of concurrent sessions? The answer is the Model Context Protocol (MCP). In Model Context Protocol in Production, you will go beyond the documentation to master the open standard that is redefining Agentic Engineering. This book is a comprehensive, code-first guide to building the secure, scalable, and observable connectors that turn "chatbots" into powerful operational tools. Inside, you will discover how to:Architect the "Zero Trust" Agent: Move beyond brittle API wrappers. Learn to build "N x M" connectors that work seamlessly across Claude Desktop, IDEs, and custom web hosts without rewriting code.Master the Protocol: Deep dive into the JSON-RPC wire format, the Capability Handshake, and the lifecycle of a tool call. Understand the physics of the system before you build on top of it.Build Production-Grade Servers:Python (FastMCP): Construct rapid data connectors using uv, Pydantic, and AsyncPG for high-performance SQL and Vector interactions.TypeScript (Node.js): Engineer enterprise-scale gateways using Zod and strict typing, capable of handling massive concurrency.Solve the "Hard" Problems: Implement Human-in-the-Loop approval flows for dangerous actions, manage long-running async jobs that outlast standard timeouts, and handle binary payloads like images and PDFs.Secure Your Infrastructure: Implement defense-in-depth strategies, including input sanitization, OAuth 2.0 authentication, and Role-Based Access Control (RBAC) to prevent Prompt Injection from becoming Remote Code Execution.Deploy at Scale: Containerize your agents with Docker best practices, deploy to Serverless environments (AWS Lambda/Vercel), and manage load balancing for high-traffic fleets.Real-World Blueprints Included:Stop building toys. This book provides complete, architectural blueprints for four essential enterprise agents:The DevOps Connector: A safe Bastion service for debugging Kubernetes pods and Docker containers.The SaaS Wrapper: A unified bridge for managing Jira tickets and Slack notifications.The Knowledge Base: A hybrid search engine combining live Notion API reads with local Obsidian vector indexing.The Analyst: A secure, Docker-isolated Python sandbox for executing arbitrary data science code.Whether you are a backend engineer tasked with "adding AI" to your platform, or a startup founder building the next great agentic tool, this book is your blueprint. Stop writing scripts. Start building the standard.
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