We build a complete MCPAgentLoop that replicates how real AI agents interact with colab-mcp: it receives a task, plans a sequence of tool calls, dispatches them to a NotebookState manager, inspects outputs, and iterates until the notebook is fully built. We watch the agent run four iterations, which add a markdown title cell, import libraries, generate data, compute descriptive statistics, and write a summary, producing a four-cell notebook entirely through tool calls, with every execution result printed inline. We then print a full-production integration template showing both the zero-code path (a JSON config block for Claude Code or the Gemini CLI) and the custom-agent path (a complete Anthropic API loop with tool definitions, message history management, and tool-result wiring).
Россиянку обнаружили без признаков жизни в гостиничном номере в Грузии14:52
。关于这个话题，whatsapp网页版提供了深入分析
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大多数指令都是单个字符，所有数字都是整数。