kgraph-mcp-agent-platform / docs /progress /mvp3_sprint1_tasks.md
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πŸ† Multi-Track Hackathon Submission
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MVP 3 Sprint 1: Interactive Prompt Filling & Dynamic UI Generation

Sprint Goal: Transform the current display-only input variable system into interactive input fields that allow users to provide values for prompt variables, preparing for simulated execution.

Duration: 2-3 hours
Priority: HIGH (New MVP phase)
Dependencies: MVP 2 completion (current status)


🎯 Sprint 1 Objectives

  • Implement dynamic generation of interactive gr.Textbox components based on selected prompt variables
  • Enable users to provide actual input values for prompt templates
  • Prepare UI foundation for MVP 3 Sprint 2 (simulated execution)
  • Maintain backward compatibility with existing MVP 2 functionality

πŸ“‹ Task Breakdown

Task 26: Design Strategy for Dynamic UI Components

  • Task ID: 26
  • Status: Todo
  • Priority: HIGH
  • Estimated Time: 45-60 minutes
  • Dependencies: Task 25 (Sprint 5 completion)
  • Description: Research and finalize the strategy for dynamically creating interactive input components in Gradio based on prompt variables. Document the chosen approach and implementation plan.

Strategy Options Analysis:

  1. Pre-defined Max Inputs (RECOMMENDED): Create fixed number of gr.Textbox components, show/hide based on prompt variables
  2. Dynamic Group Rendering: Use gr.Group with conditional updates (more complex)
  3. HTML/JS Injection: Less ideal for Gradio's reactive model

Decision Criteria:

  • Implementation speed for hackathon timeline
  • Gradio state management simplicity
  • Maintenance and debugging ease
  • User experience quality

Deliverables:

  • Strategy documentation with chosen approach
  • Implementation plan outline
  • UI/UX mockup or description
  • Technical requirements specification

Acceptance Criteria:

  1. Clear strategy chosen and documented with rationale
  2. Maximum number of input fields determined (recommendation: 5)
  3. UI flow and user interaction pattern defined
  4. Technical implementation approach detailed
  5. Integration points with existing code identified

Task 27: Implement Dynamic Input Fields in Gradio UI

  • Task ID: 27
  • Status: Todo
  • Priority: HIGH
  • Estimated Time: 90-120 minutes
  • Dependencies: Task 26 completion
  • Description: Implement the dynamic input field generation system in app.py, modify the Gradio UI layout, and update the handle_find_tools function to manage input field state.

Implementation Plan:

Phase 1: UI Layout Modification

  • Add predefined gr.Textbox components to gr.Blocks() layout
  • Create container group for input fields with visibility control
  • Add "Execute Plan" button (initially hidden)
  • Add execution results display area

Phase 2: Backend Logic Update

  • Modify handle_find_tools return signature for multiple outputs
  • Implement input field state management logic
  • Create gr.update() dictionaries for dynamic field control
  • Handle edge cases (no variables, too many variables, etc.)

Phase 3: Event Wiring

  • Update find_button.click() event outputs mapping
  • Ensure proper state synchronization
  • Test Gradio reactive updates

Technical Specifications:

# Configuration
MAX_PROMPT_INPUTS = 5  # Recommended maximum

# New UI Components to Add:
- prompt_inputs_group (gr.Column with visibility control)
- prompt_input_fields[] (list of gr.Textbox components)
- execute_button (gr.Button for future sprint)
- execution_output_display (gr.Markdown for results)

# Updated Return Signature:
handle_find_tools() -> Tuple[
    dict,  # results_output_json (existing)
    gr.update,  # prompt_inputs_group visibility
    *[gr.update],  # individual textbox updates (MAX_PROMPT_INPUTS)
    gr.update,  # execute_button visibility
    gr.update  # execution_output_display (clear)
]

Acceptance Criteria:

  1. UI dynamically shows correct number of input fields based on selected prompt
  2. Input fields have appropriate labels, placeholders, and validation
  3. Unused input fields are properly hidden
  4. Execute button appears when plan is selected
  5. All existing MVP 2 functionality preserved
  6. No Gradio errors or state inconsistencies
  7. Responsive design maintained across different screen sizes

Task 28: Manual UI Testing & Integration Validation

  • Task ID: 28
  • Status: Todo
  • Priority: MEDIUM
  • Estimated Time: 60-75 minutes
  • Dependencies: Task 27 completion
  • Description: Comprehensive testing of the dynamic input field system, validation of user interactions, and final sprint quality assurance.

Testing Scenarios:

Core Functionality Tests:

  1. Zero Variables: Query that selects prompt with no input variables
  2. Single Variable: Prompt requiring one input (e.g., text content)
  3. Multiple Variables: Prompt with 2-3 input variables
  4. Maximum Variables: Prompt with 5+ variables (test overflow handling)
  5. Variable Switching: Quick succession of different prompts

Edge Case Tests:

  1. Empty Query Handling: Ensure input fields are properly hidden
  2. No Results: Verify fields hidden when no tools found
  3. Error States: Test behavior during agent errors
  4. Browser Compatibility: Test in different browsers/window sizes
  5. Mobile Responsiveness: Verify UI works on smaller screens

User Experience Tests:

  1. Label Clarity: Verify input field labels are descriptive
  2. Placeholder Quality: Check placeholder text is helpful
  3. Visual Design: Ensure consistent styling with MVP 2
  4. Interaction Flow: Test complete user journey from query to inputs
  5. Performance: Verify smooth transitions and updates

Quality Assurance:

  • just install - Dependencies current
  • just lint - Code quality standards
  • just format - Consistent formatting
  • just type-check - Type safety validation
  • just test - All existing tests pass
  • Manual browser testing - UI functionality

Sprint Completion:

  • Commit changes with conventional commit message
  • Push to GitHub and verify CI pipeline
  • Update task status and documentation
  • Prepare handoff notes for Sprint 2

Acceptance Criteria:

  1. All test scenarios pass successfully
  2. UI provides smooth, intuitive user experience
  3. No regressions in existing MVP 2 functionality
  4. Performance remains optimal (<400ms response times)
  5. Code quality gates pass (linting, type checking, tests)
  6. Documentation updated with new features
  7. Sprint 1 marked complete and ready for Sprint 2

🎯 Sprint 1 Success Criteria

Technical Deliverables:

  • Dynamic input field generation system operational
  • 5 predefined input fields with show/hide functionality
  • Execute button infrastructure in place
  • Backward compatibility with MVP 2 maintained
  • All quality gates passing

User Experience Goals:

  • Intuitive input field interaction
  • Clear labels and helpful placeholders
  • Smooth visual transitions
  • Consistent design language
  • Mobile-friendly responsive design

Quality Standards:

  • No Gradio console errors
  • All existing tests passing
  • New code follows project standards
  • Documentation updated appropriately
  • CI/CD pipeline green

πŸ”„ Next Steps (Sprint 2 Preview)

After Sprint 1 completion, MVP 3 Sprint 2 will focus on:

  1. Input Collection Logic - Gather user input from dynamic fields
  2. ExecutorAgent Implementation - Create simulated execution engine
  3. Output Generation - Mock realistic tool execution results
  4. Result Display Enhancement - Show simulated execution output

πŸ“Š Risk Assessment

Low Risk:

  • UI component implementation (familiar with Gradio)
  • Integration with existing codebase (well-structured)
  • Testing methodology (established patterns)

Medium Risk:

  • Gradio state management complexity
  • Performance with multiple dynamic updates
  • Mobile responsiveness edge cases

Mitigation Strategies:

  • Thorough testing with different input combinations
  • Performance monitoring during development
  • Progressive enhancement approach for complex features

Sprint Priority: HIGH - Foundation for MVP 3 interactivity
Confidence Level: HIGH - Building on solid MVP 2 foundation
Estimated Sprint Completion: 95% confidence in 2-3 hours