Mcp

In less than two years since its launch, the Model Context Protocol (MCP) has transformed from an internal Anthropic initiative into an open industry standard. Now managed by the Agentic AI Foundation under the Linux Foundation, MCP is redefining how we design software systems. If TCP/IP connects computers, and REST connects microservices, then the MCP protocol was born to directly connect LLMs (Large Language Models) and AI Agents with real-world data and tools. ...

To master a protocol, you must understand its DNA. Before we write Go code in the upcoming parts, we need to dismantle the architecture of the Model Context Protocol (MCP). Underneath the complex AI workflows, MCP is surprisingly simple and elegant. It is built on top of the JSON-RPC 2.0 specification, a stateless, lightweight remote procedure call protocol. When comparing modern system architectures, especially high-throughput environments discussed in the Shopee Architecture Series, engineers often lean towards binary protocols like gRPC. However, MCP chose JSON-RPC for a very specific reason: LLMs natively understand JSON, and debugging a prompt trace is exponentially easier when the payload is human-readable text rather than compiled Protocol Buffers. ...

In 2025, METR — an AI safety and capability research organization — ran a rigorous randomized controlled trial. Sixteen experienced open-source developers worked on 246 real-world tasks, each randomly assigned to either use AI coding tools freely or not at all. The result was counterintuitive: developers using AI tools were 19% slower on complex tasks. Before the study, those same developers predicted AI would make them 24% faster. After completing the experiment — still believing they had gone faster — their subjective confidence remained completely unshaken. ...

The Biggest Shift in 2026: Context Over Phrasing If you have been writing prompts by carefully choosing words and hoping the model “gets it,” you are operating on a 2024 mental model. In 2026, the industry consensus is clear: the quality of the context you assemble matters far more than the phrasing of your instructions. This shift has a name: Context Engineering. What Is Context Engineering? Context Engineering is the discipline of designing systems that assemble the right information into the model’s context window at the right time. ...

1. The Decline of Static RAG In the previous 5 parts, we built a perfect RAG machine: real-time data (CDC), absolute security, and strict authorization. But no matter how perfect, traditional RAG suffers from a fatal flaw: It only knows how to “Read” and “Speak”, not how to “Do”. If you ask a RAG system: “Check if the server is overloaded, and if so, automatically boot up 2 more servers”, it will be completely powerless. RAG is a Static Pipeline running on a one-way street. ...

Answer-first: Architecting dynamic generative UI applications with Model Context Protocol (MCP): dynamic registries, client-agent state synchronization, security, and a11y. The first generation of AI-powered chat interfaces followed a simple pattern: the user types a message, the LLM generates text, the UI renders text. The second generation added tool calls — the LLM could invoke functions and render the results as text. The third generation — Generative UI — goes further: the LLM generates not just text responses but interactive UI components that are rendered directly in the browser, enabling experiences that feel less like chatting with a text box and more like using a responsive, intelligent application. ...

In this edition of our tech radar, we break down the developments of May 28, 2026. Following the May 26 radar on AI Ethics and Anthropic’s $30B funding, the landscape of Enterprise AI has experienced a seismic shift. We are officially seeing the end of the “Model-as-a-Service” era, giving way to massive B2B integration plays and autonomous “Agent-as-a-Service” workflows. Here are the critical technical and strategic breakdowns of today’s signals. 1. The Enterprise Pivot: OpenAI Launches DeployCo ($4B) For the past year, OpenAI has faced immense pressure in the enterprise sector from Anthropic, whose Claude models have become the de-facto standard for corporate compliance and complex coding tasks. In a decisive counter-move, OpenAI has officially launched DeployCo (OpenAI Deployment Company). ...

Today is May 22, 2026, the week following Google I/O, witnessing a massive transition from AI Copilots (limited to summarizing and recommending) to autonomous AI Agents (capable of proactive execution). While developers are excited about Gemini Intelligence and Autonomous AI Swarm architectures, the cybersecurity community faces a major challenge: How do we control these non-human actors? Today’s Radar bulletin dissects the strategic moves from the NSA, Microsoft, and Zscaler in establishing security boundaries for the “Agentic Web”. ...

Today is May 19, 2026. Google I/O 2026 is underway at the Shoreline Amphitheatre, Mountain View. Sundar Pichai’s main keynote started at 10:00 AM PT; the Developer Keynote—the most crucial session for engineering teams—commenced at 1:30 PM PT. If you haven’t read yesterday’s radar on K8s v1.36 and Google I/O T-1, that is the necessary context before reading this. This is not a typical product launch event. It is a platform architecture commitment event: Google is betting simultaneously on three tiers—the OS layer (Gemini Intelligence), the backend layer (Firebase rebuilt + Antigravity), and the developer toolchain layer (Jules + Googlebooks). Notably, both OpenAI and Anthropic executed major structural moves on the very same day—a deliberate timing choice. The broader context regarding the costs and risks of agentic AI workloads was analyzed in the May 15 radar. ...

Answer-first: Field-tested patterns for AI APIs in production: OAuth 2.1 agent identity, prompt versioning with CI gates, and an honest 2025 AI predictions scorecard. Running AI APIs in production for the past 18 months has produced three lessons that I did not find in any “getting started with LLMs” tutorial. They emerged from incidents, postmortems, and that specific kind of 2 AM Slack message where a word you never wanted to see — “silent,” as in “silent failure” — appears in a production context. ...