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Guidelines for enhancing the MCP tool API and adding new features.
🎨 Intelligent UI component design selection tool built on Model Context Protocol (MCP). Compare 3 designs with automatic CSS framework detection, adaptive layouts, and real-time selection. Perfect for design systems and A/B testing.
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Guidelines for enhancing the MCP tool API and adding new features.
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Clarify-first strategy: detect ambiguity, ask targeted questions, expand simple prompts into detailed specifications.
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Complete Cursor 2.2 native tools reference: search, edit, run, and advanced features for agentic workflows.
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Explains the workflow and process of the design selection tool.
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This guide covers the development workflow for the Rakit UI AI project.
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DevOps and infrastructure: Docker, Kubernetes, Terraform, CI/CD pipelines, and cloud deployment patterns.
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Flutter development: widget patterns, state management, Dart best practices, and platform channel integration.
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Go development: error handling, interfaces, concurrency, modules, and idiomatic patterns.
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Universal programming patterns: SOLID principles, design patterns, clean code, architecture decisions applicable across all languages.
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The project implements an MCP (Model Context Protocol) tool following the framework's required structure.
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MiniMax M2.1 agentic-first core behavior: Opus 4.5-style reasoning, 1M context optimization, verification-first development, and production-quality code generation.
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MiniMax M2.1 verification protocols: code compilation, UI testing, syntax correctness, and production-readiness checks.
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MiniMax M2.1 MCP tools: websearch for version checking and real-time info, understandimage for visual analysis, Context7 for documentation.
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Cross-platform mobile development: shared architecture, platform-specific patterns, and native integration strategies.
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Guidelines for optimizing the performance of the design selection tool.
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High-level overview of the Rakit UI AI project structure and components.
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Python development: modern Python, async/await, FastAPI, Django, data science, and ML patterns.
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Rust development: ownership, lifetimes, error handling, async patterns, and cargo workflows.
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This rule outlines security considerations for the Rakit UI AI project.
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The design selection tool uses a local HTTP server to display design options and handle user selection. This is implemented in src/tools/utils/serverUtils.ts.
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Swift development: SwiftUI, Combine, async/await, iOS patterns, and Apple platform conventions.
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Comprehensive testing approach for the Rakit UI AI design selection tool.
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The UI for design selection is generated using a template system defined in src/tools/templates/designSelection.ts.
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Modern web development: JavaScript, TypeScript, React, Vue, Next.js, Node.js, HTML/CSS, and web APIs.