This article discusses common architectural pitfalls that cause Model Context Protocol (MCP) servers to fail under production load. It highlights issues like in-process state, synchronous flows, lack of rate limiting, and tight coupling to dependencies. The author proposes solutions such as stateless MCP servers with external state management, asynchronous processing via queues, implementing circuit breakers and rate limiting, aggressive caching, and robust observability. AI
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IMPACT Provides best practices for building scalable and resilient infrastructure for AI model serving.
RANK_REASON The article provides architectural patterns for scaling specific types of servers (MCP), which is a technical implementation detail rather than a core AI release or significant industry event.