shadcn-vue-mcp

# shadcn-vue UI component generates MCP Server development roadmap ## 🎯 Core goal confirmation - [x] Define specific standards for "high-quality UI components" (accessibility, performance, consistency, maintainability, developer experience) - [x] Determine the target user group (developer experience level, usage scenarios, core pain points) - [x] Formulate component quality evaluation indicators, developer experience (DX) indicators and acceptance criteria ## 📋 Phase 1: Infrastructure optimization & core DX foundation ### Component data management - [ ] Create component classification and labeling system (function, scenario, atomicity, etc.) - [ ] Implement component dependency graph analysis and visualization prototype - [ ] Added a preliminary evaluation mechanism for component complexity and maintainability ### Intelligent component filtering optimization - [ ] Improved component filtering algorithm, supporting preliminary semantic analysis based on user descriptions - [ ] Added component combination suggestions (based on common patterns and component relationships) - [ ] Implemented simple component recommendations based on project file context - [ ] Supported multi-language description parsing (Chinese and English) - [ ] Added component usage frequency statistics and recommendation weights ### Documentation and example enhancements - [ ] Extended component documentation acquisition, including best practice cases and code snippets - [ ] Added anti-patterns and common error examples and avoidance solutions - [ ] Integrate shadcn-vue official update subscription and change notifications - [ ] Implemented local document caching and fast retrieval mechanism - [ ] Added detailed descriptions and visual preview entries for component variants and configuration options ### Core developer experience (DX) foundation - [ ] Implemented core IDE plug-in functions (such as VS Code), supporting component list display, search, and basic code snippet insertion - [ ] Provide a preliminary real-time preview of generated code (in IDE or web interface) - [ ] Establish user feedback channels and issue tracking systems ## 📋 Phase 2: Improve code generation quality & practice-driven intelligence ### Intelligent code generation - [ ] Implement preliminary code generation based on the existing code style of the project (indentation, naming conventions, etc.) - [ ] Strengthen TypeScript type safety checks and more accurate type generation - [ ] Support automatic application of responsive design patterns and option prompts - [ ] Implement component props validation, intelligent recommendation of default values, and custom configuration interfaces - [ ] Add component state management mode suggestions (ref vs reactive vs Pinia store fragments) - [ ] Provide code generation options such as Composition API / Options API ### Accessibility (A11y) integration - [ ] Automatically add core ARIA attributes and semantic tags - [ ] Implement basic checks and prompts for keyboard navigation support - [ ] Add color contrast basic validation tool integration - [ ] Integrated screen reader compatibility checklist and common problem tips - [ ] Implement focus management best practice reminders and provide brief explanations of A11y design decisions (Explanatory AI L1) ### Performance optimization - [ ] Code splitting and lazy loading suggestions (based on component size and usage scenarios) - [ ] Component rendering performance optimization point tips (such as v-if vs v-show, key usage) - [ ] Remind unnecessary re-render detection methods and optimization strategies - [ ] Add preliminary assessment and warnings on the impact of Bundle size - [ ] Implement Tree-shaking friendly code generation practices and provide relevant explanations (Explanatory AI L1) ### IDE integration deepening - [ ] Implement richer component configuration interface in IDE to replace plain text input - [ ] Support direct preview of the effects of different variants and props of components in IDE - [ ] Implement one-click import of code snippets and automatic dependency processing ## 📋 Phase 3: Advanced feature development & contextual intelligence ### Context-aware generation - [ ] Achieve in-depth project context understanding (analyze existing code style, component library usage patterns, design token integration) - [ ] Automatically adapt and recommend component naming conventions based on project context - [ ] Add project-specific component variant configuration persistence and recommendations - [ ] Support analysis of existing project components and provide extension and customization suggestions - [ ] Make preliminary personalized recommendations based on developer historical preferences (e.g., commonly used props combinations, state management patterns) ### Multimodal input support - [ ] Add design draft image parsing capabilities (Figma, Sketch, etc.) to extract component structure and style information - [ ] Support conversion of hand-drawn sketches to basic component structures - [ ] Implement component reconstruction assistance for existing website screenshots - [ ] Add component wireframe or low-fidelity prototype rapid generation - [ ] Preliminary support for mapping natural language descriptions to simple component structures and option recommendations ### Test integration - [ ] Automatically generate component basic unit tests (Vitest/Jest) - [ ] Add visual regression testing tool integration suggestions and configuration assistance - [ ] Implement basic test code snippet generation for component interaction behavior - [ ] Integrate Storybook stories to automatically create or update functions - [ ] Add basic code generation for E2E test scenarios (Cypress/Playwright) ## 📋 Phase 4: Collaboration, version management & reliability (basically the same as before, can be enhanced) ### Component version control and evolution - [ ] Implement version management and historical traceability of generated component code snippets - [ ] Add compatibility check and impact analysis of component API changes (props, events, slots) - [ ] Support component rollback, version comparison and change log automatic generation framework - [ ] Implement intelligent prompts and assisted migration for component updates (such as dependent library upgrades) - [ ] Establish a recommendation mechanism for component deprecation and refactoring ### Team collaboration function - [ ] Add review and approval workflow suggestions for component design and code (integrate external tools) - [ ] Implement component usage statistics and feedback collection mechanism within the team - [ ] Support component library contribution guide and best practice document template generation - [ ] Add synchronization checks and prompts for component design specifications and project tokens - [ ] Create a prototype of a component "recipe" or "pattern" library shared by the team ### Quality assurance process - [ ] Deeply integrate code quality checks (ESLint, Prettier, Stylelint) and provide one-click repair suggestions - [ ] Add automatic verification rules and tools for component design consistency - [ ] Integrate automated accessibility test suites (such as Axe) - [ ] Implement framework integration for performance benchmarking and monitoring - [ ] Create a prototype of a component quality score and health dashboard ## 📋 Phase 5: User experience leap & smart co-pilot ### Smart co-pilot and proactive insights - [ ] Proactively provide component usage suggestions, potential problem warnings, and best practice context prompts - [ ] Develop advanced component configuration wizards to intelligently recommend configuration combinations based on user scenarios - [ ] Support progressive component complexity improvement and display advanced options on demand - [ ] Implement intelligent error repair suggestions and provide solutions to common build errors or configuration conflicts - [ ] Create personalized component learning paths and advanced usage recommendations - [ ] Proactively provide architecture rationality analysis and refactoring suggestions (for complex component combinations) - [ ] Implement "Explanatory AI": Provide clear explanations of "why this is done" for key build decisions, A11y, and performance optimization points ### Visual interface and interaction - [ ] Develop a full-featured component real-time editor that supports two-way binding and props adjustment - [ ] Add visual analysis tools for component dependencies, props transfer, and event flows - [ ] Implement a drag-and-drop component combination interface for rapid prototyping and complex component construction - [ ] Create a dynamic visual editor for component configuration to reflect changes in real time - [ ] Enhance real-time preview and HMR (hot module replacement) to provide an ultimate instant feedback experience - [ ] Implement visual comparison of component version history and changes ### Documentation and tutorials - [ ] Automatically generate interactive, customizable component usage documentation - [ ] Create scenario-based, task-driven best practice guides and tutorials - [ ] Add intelligent retrieval and contextual answer system for FAQs - [ ] Generate interactive tutorials to guide users to master advanced functions - [ ] Establish a rich component sample library and template library, and support one-click application to projects ## 📋 Phase 6: Ecosystem construction, AI evolution & platformization ### Open platform and community ecology - [ ] Design and publish a stable and powerful plug-in architecture and developer API - [ ] Establish an online sharing platform for "component recipes/patterns" to support community contributions, discovery, ratings and subscriptions - [ ] Implement deep integration with mainstream version control (Git) processes (such as automatically generating PR descriptions and change suggestions) - [ ] Support developers to customize code generation templates and rule engines - [ ] Encourage and incubate MCP Server-based API third-party tools and applications ### AI-driven continuous evolution - [ ] Implement advanced natural language understanding, support multi-round conversational component generation and adjustment for complex requirements - [ ] Introduce user behavior analysis and feedback learning, and continuously optimize the personalization and accuracy of AI recommendation and generation logic - [ ] Study the self-evolution and maintenance capabilities of code: when the dependent library is updated or a better mode appears, actively prompt and assist project component upgrade/refactoring - [ ] Support design trend analysis and incorporate it into component generation suggestions - [ ] Create an AI-driven component optimization assistant to provide continuous and personalized improvement suggestions ### Ecosystem integration and expansion - [ ] Support intelligent migration assistance from other UI framework components (such as React AntD/Material-UI) to shadcn-vue - [ ] Deeply integrate mainstream design tools (Figma, Adobe XD, Sketch) to achieve more accurate design-to-code conversion - [ ] Add component output and integration support for CMS, low-code/no-code platforms - [ ] Implement automated component generation and adaptation based on external API data sources - [ ] Support a wider range of themes and design system import, export and conversion ### Performance monitoring and analysis - [ ] Add performance monitoring and bottleneck analysis of the component generation process - [ ] Implement analysis of user operation behavior in MCP Server for product iteration - [ ] Create quality and performance trend analysis of components in actual projects (through feedback or integration) - [ ] Add A/B testing support to verify the effectiveness of new features or generation strategies - [ ] Establish a complete, data-driven continuous improvement feedback loop ## 🔧 Technical debt and maintenance - [ ] Regularly refactor existing code architecture to improve scalability, maintainability and performance - [ ] Add a comprehensive error handling, logging, monitoring and alarm system - [ ] Implement flexible configuration files and environment management mechanisms - [ ] Create complete, easy-to-understand, and real-time updated API documentation and usage guides - [ ] Establish and strictly implement automated testing (unit, integration, E2E) and CI/CD processes ## 📊 Success indicator definition - [ ] Define quantitative evaluation criteria for component generation quality (code quality, A11y score, performance indicators) - [ ] Establish user satisfaction and developer experience (DX) measurement mechanism (NPS, DAU/MAU, task completion rate, feedback score) - [ ] Create performance benchmarks and goals (generation speed, resource consumption, output component performance) - [ ] Set engineering efficiency indicators such as code coverage, test pass rate and bug density - [ ] Establish long-term maintenance, version iteration and community activity goals