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AI Engineer’s Handbook to MCP Architecture
Addressing the MCP Hype
Around mid-December last year I told myself I would switch off from consuming any more AI news (hype in general) for a while. In India this is the season when most clients are away, sales teams are quiet, and we finally get a chance to chip away at the tech debt that piles up during the rush months. No late-night alerts, no Teams notifications (ting ting ting!), just peaceful bug fixes and refactors.
But the noise refused to stay muted. My feed kept lighting up with fresh claims: DeepSeek’s R1 models hit late-December previews, the full release followed in January, and the blogs were already calling it “OpenAI-level reasoning at a bargain”, while skeptics yelled “psyops” in the threads. Teams were still on holiday, yet the AI news pipe kept pumping at full pressure. It felt as if a swarm of agents had taken over social media just to keep the hype alive.
The rise of MCP
Then the timeline broke its silence. Post after post shouted, “MCP (Model Context Protocol) is the next big connector!” I scrolled past the first few; December is demo-season, when half-baked ideas pop up and vanish before February. But not this one. By New Year’s Eve, the hashtag was everywhere, and by the second week of January, even the weekend hackers were live-streaming…
