DevOps AI Toolkit

# PRD: Web UI for MCP Server Interaction **Issue**: [#109](https://github.com/vfarcic/dot-ai/issues/109) **Created**: 2025-09-17 **Status**: Draft **Priority**: Medium --- ## Executive Summary Create a web-based user interface that acts as an MCP client, providing chat-like interactions with visual data representations. This will make the MCP server functionality accessible through a browser-based GUI, offering an alternative to CLI-based MCP clients like Claude Code. ## Problem Statement ### Current State - MCP server provides powerful Kubernetes AI capabilities through CLI clients (Claude Code, Cursor) - Users must use MCP-compatible editors or command-line tools to interact with the system - No web-based interface exists for users who prefer browser-based interactions - Visual data representation is limited in text-based CLI environments ### Pain Points - **Accessibility Barrier**: Users unfamiliar with CLI tools or MCP clients can't easily access the system - **Limited Visualization**: Complex Kubernetes data and AI recommendations are hard to visualize in text-only interfaces - **User Experience Gap**: No graphical interface for exploring cluster capabilities, solutions, and deployments - **Sharing & Collaboration**: Difficult to share visual insights or collaborate on deployment decisions ## Success Criteria ### User Experience Success - [ ] Users can interact with all MCP functionality through a web browser - [ ] Chat interface provides intuitive conversation flow similar to Claude Code - [ ] Complex data (cluster capabilities, recommendations, manifests) displays with visual representations - [ ] Response time for MCP tool calls under 3 seconds for standard operations ### Technical Success - [ ] Web UI successfully connects to and communicates with existing MCP server - [ ] All current MCP tools accessible through the web interface - [ ] Visual components render Kubernetes resources, deployment statuses, and AI recommendations - [ ] System handles concurrent users without performance degradation ### Business Success - [ ] Increased MCP adoption due to improved accessibility - [ ] Positive user feedback on visual data representation - [ ] Documentation demonstrates clear setup and usage workflows ## Target Users ### Primary Users - **DevOps Engineers**: Need visual overview of cluster capabilities and deployment status - **Platform Engineers**: Want graphical interface for managing AI recommendations and solutions - **Developers**: Prefer browser-based tools over CLI for exploring Kubernetes deployment options ### Secondary Users - **Engineering Managers**: Need visual dashboards for understanding deployment patterns and cluster usage - **Site Reliability Engineers**: Want graphical monitoring of AI-assisted deployment outcomes ## User Stories & Workflows ### Core User Journey ``` User opens Web UI → Authenticated session → Chat interface loads → User types intent ("deploy web app") → MCP processes request → Visual solution cards displayed → User configures through forms → Deployment manifests shown with syntax highlighting → Deploy button triggers MCP deployment → Status updates shown graphically ``` ### Detailed User Stories **As a DevOps Engineer**, I want to: - See cluster capabilities as interactive cards/charts rather than text lists - Configure deployment solutions through visual forms instead of typing answers - View generated Kubernetes manifests with syntax highlighting and collapsible sections - Monitor deployment progress through visual status indicators **As a Platform Engineer**, I want to: - Browse solution patterns through a visual catalog interface - Create and manage organizational patterns using drag-and-drop or form interfaces - Visualize resource dependencies as interactive graphs - Share deployment configurations via shareable URLs **As a Developer**, I want to: - Chat with the AI in a familiar interface similar to ChatGPT/Claude - See deployment recommendations as comparison tables or decision trees - Preview how my application will look in the cluster before deploying - Get visual feedback on deployment success/failure with actionable next steps ## Technical Architecture ### High-Level Architecture ``` [Web Browser] ↔ [Frontend (React/Vue/Angular)] ↔ [MCP Protocol] ↔ [Existing MCP Server] ↔ [Kubernetes API] ``` ### Component Breakdown **Frontend Application (New Repository)** - **Chat Interface**: Message threads, typing indicators, message history - **Visual Components**: Charts, graphs, forms, syntax highlighting, status dashboards - **MCP Client**: WebSocket/HTTP connection to MCP server, protocol handling - **State Management**: User sessions, conversation history, deployment tracking **Backend Integration** - **MCP Server**: Existing server with no modifications required - **Authentication**: Session management for web users (if needed) - **WebSocket Gateway**: Real-time communication layer (may be part of frontend) ### Technology Stack Recommendations - **Frontend**: React/Next.js or Vue.js/Nuxt.js for rich UI components - **MCP Communication**: WebSocket client for real-time chat experience - **Visualization**: D3.js, Chart.js, or similar for data visualization - **UI Framework**: Tailwind CSS or Material-UI for consistent design - **State Management**: Redux/Zustand or Vuex for complex state handling ## Feature Requirements ### Must-Have Features (MVP) #### Chat Interface - [ ] Text input with send button and Enter key support - [ ] Message history with user/AI message distinction - [ ] Typing indicators during MCP tool execution - [ ] Message timestamps and session persistence #### MCP Tool Integration - [ ] All existing MCP tools accessible through chat commands - [ ] Support for `recommend`, `chooseSolution`, `answerQuestion`, `generateManifests`, `deployManifests` - [ ] Support for `manageOrgData`, `remediate`, `testDocs` workflows #### Visual Data Representation - [ ] Cluster capabilities displayed as interactive cards/tables - [ ] Solution configurations shown as forms instead of Q&A - [ ] Kubernetes manifests with syntax highlighting and collapsible sections - [ ] Deployment status with progress indicators and success/error states #### Core Workflows - [ ] Solution recommendation → configuration → deployment flow - [ ] Organizational pattern management with visual editors - [ ] Remediation workflows with issue visualization and step-by-step guidance - [ ] Documentation testing with results presentation ### Should-Have Features (Phase 2) #### Enhanced Visualization - [ ] Resource dependency graphs using network diagrams - [ ] Cluster topology visualization showing nodes, pods, services - [ ] Deployment timeline with historical data - [ ] Comparison tables for solution alternatives #### Collaboration Features - [ ] Shareable conversation URLs - [ ] Export conversation/deployment configs - [ ] Team workspaces for shared patterns and solutions #### Advanced UX - [ ] Drag-and-drop solution configuration - [ ] Auto-complete for Kubernetes resources and common patterns - [ ] Saved conversation templates and quick actions - [ ] Dark/light mode theming ### Could-Have Features (Future) - [ ] Mobile-responsive design for tablet access - [ ] Integration with Git repositories for manifest storage - [ ] Webhook notifications for deployment events - [ ] Multi-cluster support with cluster switching ## Implementation Milestones ### Milestone 1: Core Chat Interface (4-6 weeks) - [ ] Set up new repository with frontend framework - [ ] Implement basic chat UI with message rendering - [ ] Establish MCP protocol communication - [ ] Basic authentication and session management - [ ] Simple text-based MCP tool invocation working **Success Criteria**: User can have text-based conversations with MCP through web interface ### Milestone 2: Visual Components Foundation (3-4 weeks) - [ ] Implement syntax highlighting for Kubernetes manifests - [ ] Create card-based layouts for cluster capabilities and solutions - [ ] Add form components for solution configuration - [ ] Implement progress indicators and status displays **Success Criteria**: Key data types display visually instead of raw text ### Milestone 3: Complete MCP Tool Integration (3-4 weeks) - [ ] All MCP tools accessible and working through web interface - [ ] Full solution recommendation → configuration → deployment flow - [ ] Organizational pattern management workflows - [ ] Error handling and user feedback systems **Success Criteria**: Web UI provides feature parity with MCP CLI functionality ### Milestone 4: Enhanced User Experience (2-3 weeks) - [ ] Responsive design for different screen sizes - [ ] Message history persistence and session management - [ ] Copy/export functionality for manifests and configurations - [ ] Performance optimization for large data sets **Success Criteria**: Professional-grade user experience comparable to modern web applications ### Milestone 5: Testing & Documentation (2-3 weeks) - [ ] Comprehensive test suite for frontend components - [ ] Integration tests with MCP server - [ ] Complete user documentation and setup guides - [ ] Deployment documentation for production environments **Success Criteria**: System ready for production use with complete documentation ### Milestone 6: Production Deployment (1-2 weeks) - [ ] Production deployment pipeline setup - [ ] Security review and hardening - [ ] Performance monitoring and analytics - [ ] User feedback collection mechanisms **Success Criteria**: Web UI publicly available and monitored in production ## Technical Considerations ### Security - **Authentication**: Determine if web interface needs user authentication separate from MCP server - **Authorization**: Ensure web users have same permissions model as CLI users - **Network Security**: Secure WebSocket/HTTP connections, CORS policies - **Input Validation**: Sanitize user inputs before sending to MCP server ### Performance - **Real-time Updates**: WebSocket connections for responsive chat experience - **Large Data Handling**: Pagination or virtualization for large cluster capability lists - **Caching**: Client-side caching of cluster data and solution templates - **Bundle Size**: Code splitting and lazy loading for fast initial loads ### Scalability - **Concurrent Users**: Multiple simultaneous web sessions connecting to single MCP server - **Session Management**: Persistent conversations across browser refreshes - **Resource Usage**: Frontend memory usage with large conversation histories ### Integration Challenges - **MCP Protocol**: Ensuring web client properly implements MCP communication patterns - **State Synchronization**: Keeping web UI state consistent with MCP server state - **Error Handling**: Graceful handling of MCP server disconnections or errors - **Version Compatibility**: Managing compatibility between web UI and MCP server versions ## Dependencies & Risks ### Technical Dependencies - **MCP Server Stability**: Web UI depends on existing MCP server reliability - **Kubernetes Access**: Same cluster access requirements as current MCP server - **Browser Support**: Modern browsers with WebSocket and ES6+ support required ### Project Risks - **Complexity Risk**: Web UI development may be more complex than anticipated - **Maintenance Risk**: Additional codebase to maintain alongside MCP server - **User Adoption Risk**: Users may prefer existing CLI tools over web interface - **Performance Risk**: Web interface may be slower than direct CLI interaction ### Mitigation Strategies - **Start Simple**: Begin with basic chat interface, add visual components incrementally - **Reuse Patterns**: Leverage existing web UI component libraries and patterns - **Early Feedback**: Get user feedback after each milestone to guide development - **Performance Testing**: Regular performance testing with realistic data loads ## Success Metrics ### Adoption Metrics - Number of unique users per month - Session duration and frequency of use - Conversion from CLI to Web UI users ### Usage Metrics - Most frequently used MCP tools through web interface - Average time to complete deployment workflows - User completion rates for multi-step processes ### Quality Metrics - User satisfaction surveys and feedback - Bug reports and resolution time - Performance metrics (load time, responsiveness) ## Future Considerations ### Potential Expansions - **Mobile App**: Native mobile applications for iOS/Android - **Desktop App**: Electron-based desktop application - **Enterprise Features**: SSO integration, audit logging, role-based permissions - **AI Enhancements**: Visual AI recommendations, drag-and-drop deployment building ### Integration Opportunities - **IDE Plugins**: Browser-based IDE integration - **CI/CD Integration**: Trigger deployments from CI/CD pipelines through web interface - **Monitoring Integration**: Connect to monitoring systems for deployment health visualization --- ## Change Log - **2025-09-17**: Initial PRD creation - **Status**: Draft - awaiting validation and implementation planning ## Stakeholders - **Product Owner**: [To be assigned] - **Tech Lead**: [To be assigned] - **Designer**: [To be assigned] - **QA Lead**: [To be assigned]