# MVP 2 Comprehensive Plan: "KG Suggests Actionable Tool with Prompt Template"

**Plan Created:** 2025-06-08  
**Builds Upon:** MVP 1 (Successfully Completed)  
**Target Duration:** 5 Sprints (Post-Hackathon Development)  
**Overall Goal:** Extend KGraph-MCP to suggest both relevant MCP tools AND corresponding prompt templates needed to invoke them effectively

---

## 🎯 MVP 2 Vision & Objectives

### **Strategic Goal**
Transform the KGraph-MCP from a tool discovery system into an actionable task planning system that provides both:
1. **Relevant MCP Tools** (carried over from MVP 1)
2. **Prompt Templates** with input variables for effective tool usage

### **Core Value Proposition**
- **From Discovery to Action:** Users get not just "what tool to use" but "how to use it effectively"
- **Prompt Engineering Automation:** System suggests optimized prompts for different use cases
- **Template-Driven Execution:** Structured approach to tool invocation with clear input requirements

### **Target User Experience**
```
User: "I need to analyze customer feedback sentiment"
System Response:
┌─ Tool: Sentiment Analyzer
│  Description: Analyzes text for emotional tone and sentiment
└─ Prompt: "Customer Feedback Analysis Template"
   Description: Optimized for business customer feedback analysis
   Template: "Analyze the sentiment of this customer feedback and provide a business summary: {{customer_feedback}}"
   Required Inputs: [customer_feedback]
```

---

## 🏗️ Technical Architecture Evolution

### **MVP 1 Foundation (Completed)**
```
User Query → Embedding → Tool Discovery → Tool Suggestion
```

### **MVP 2 Target Architecture**
```
User Query → Embedding → Tool Discovery → Prompt Selection → (Tool + Prompt) Suggestion
                     ↗                 ↗
              Tool Embeddings    Prompt Embeddings
```

### **New Components in MVP 2**
1. **MCPPrompt Ontology** - Structured prompt representation
2. **Prompt Knowledge Graph** - Semantic prompt storage and retrieval
3. **Enhanced Planner Agent** - Tool + Prompt selection logic
4. **Rich UI Display** - Template visualization and input variable guidance

---

## 📋 Sprint Overview

| Sprint | Focus Area | Duration | Key Deliverables |
|--------|------------|----------|------------------|
| **Sprint 1** | Prompt Ontology & KG Enhancement | 3-4 hours | MCPPrompt dataclass, prompt loading, vector indexing |
| **Sprint 2** | Enhanced Planning Logic | 2-3 hours | Tool+Prompt selection, PlannedStep structure |
| **Sprint 3** | UI Enhancement | 2-3 hours | Rich prompt display, template visualization |
| **Sprint 4** | Dynamic Input Display | 1-2 hours | Input variable visualization, UI polish |
| **Sprint 5** | Testing & Documentation | 2-3 hours | End-to-end testing, README updates, CI validation |

**Total Estimated Time:** 10-15 hours

---

## 🚀 Sprint 1: Define Prompt Ontology & Enhance KG for Prompts

**Duration:** 3-4 hours  
**Priority:** HIGH (Foundation for all subsequent work)  
**Dependencies:** MVP 1 completion

### **Sprint 1 Objectives**
- Define comprehensive `MCPPrompt` data structure
- Create rich initial prompt metadata
- Extend `InMemoryKG` for prompt management
- Implement prompt vector indexing
- Update application initialization

### **Sprint 1 Tasks**

#### **Task 1.1: Define `MCPPrompt` Ontology (Dataclass)**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 30-45 minutes
- **Dependencies:** None
- **Description:** Create comprehensive `MCPPrompt` dataclass in `kg_services/ontology.py`
- **Acceptance Criteria:**
  1. `MCPPrompt` dataclass with all required fields and type hints
  2. Proper default values and validation
  3. Compatible with JSON serialization
  4. Unit tests passing

**Implementation Specification:**
```python
@dataclass
class MCPPrompt:
    """Represents a prompt template for MCP tool usage."""
    prompt_id: str
    name: str
    description: str
    tags: List[str] = field(default_factory=list)
    target_tool_id: str  # Links to MCPTool
    template_string: str  # Template with {{variable}} placeholders
    input_variables: List[str] = field(default_factory=list)
    use_case: str = ""  # Optional: specific use case description
    difficulty_level: str = "beginner"  # beginner, intermediate, advanced
    example_inputs: Dict[str, str] = field(default_factory=dict)  # Example variable values
```

**Test Requirements:**
- `test_mcp_prompt_creation` - Basic instantiation
- `test_mcp_prompt_validation` - Field validation
- `test_mcp_prompt_serialization` - JSON compatibility

#### **Task 1.2: Create Rich Initial Prompt Metadata**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 45-60 minutes
- **Dependencies:** Task 1.1
- **Description:** Create comprehensive `data/initial_prompts.json` with diverse, high-quality prompts
- **Acceptance Criteria:**
  1. 8-12 diverse prompt templates
  2. Coverage of all tools from MVP 1
  3. Multiple prompt styles per tool (concise, detailed, specific use cases)
  4. Rich descriptions suitable for semantic embedding

**Content Requirements:**
```json
[
  {
    "prompt_id": "sentiment_customer_feedback_v1",
    "name": "Customer Feedback Sentiment Analysis",
    "description": "Analyzes customer feedback for business insights, focusing on actionable sentiment patterns and key concerns",
    "tags": ["sentiment", "customer", "business", "feedback", "analysis"],
    "target_tool_id": "sentiment_analyzer_v1",
    "template_string": "Analyze the sentiment of this customer feedback and provide a business summary:\n\nFeedback: {{customer_feedback}}\n\nPlease provide:\n1. Overall sentiment (positive/negative/neutral)\n2. Key emotional indicators\n3. Actionable business insights",
    "input_variables": ["customer_feedback"],
    "use_case": "Business customer feedback analysis",
    "difficulty_level": "beginner",
    "example_inputs": {
      "customer_feedback": "The product arrived late and the packaging was damaged, but the customer service team was very helpful in resolving the issue quickly."
    }
  }
]
```

**Quality Standards:**
- Each prompt should be semantically distinct
- Templates should be production-ready
- Descriptions optimized for embedding quality
- Multiple difficulty levels represented

#### **Task 1.3: Extend `InMemoryKG` for Prompt Storage**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 45-60 minutes
- **Dependencies:** Task 1.2
- **Description:** Add prompt management capabilities to `InMemoryKG`
- **Acceptance Criteria:**
  1. Prompt loading from JSON
  2. Prompt retrieval methods
  3. Error handling for malformed data
  4. Unit tests passing

**Implementation Requirements:**
```python
class InMemoryKG:
    def __init__(self):
        # ... existing code ...
        self.prompts: Dict[str, MCPPrompt] = {}
    
    def load_prompts_from_json(self, filepath: str) -> None:
        """Load MCPPrompt objects from JSON file."""
        # Implementation with error handling
        
    def get_prompt_by_id(self, prompt_id: str) -> Optional[MCPPrompt]:
        """Retrieve prompt by ID."""
        
    def get_prompts_for_tool(self, tool_id: str) -> List[MCPPrompt]:
        """Get all prompts targeting a specific tool."""
```

**Test Coverage:**
- Valid JSON loading
- Invalid JSON handling
- Prompt retrieval (exists/not exists)
- Tool-specific prompt filtering

#### **Task 1.4: Implement Prompt Vector Indexing**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 60-75 minutes
- **Dependencies:** Task 1.3
- **Description:** Extend vector indexing to include prompt embeddings
- **Acceptance Criteria:**
  1. Prompt embeddings generation and storage
  2. Semantic prompt search functionality
  3. Integration with existing vector search
  4. Performance optimization

**Implementation Approach:**
```python
class InMemoryKG:
    def __init__(self):
        # ... existing code ...
        self.prompt_embeddings: List[List[float]] = []
        self.prompt_ids_for_vectors: List[str] = []
    
    def build_vector_index(self, embedder: EmbeddingService) -> None:
        """Build vector index for both tools and prompts."""
        # ... existing tool indexing ...
        
        # Prompt indexing
        for prompt_id, prompt in self.prompts.items():
            prompt_text = self._create_prompt_embedding_text(prompt)
            embedding = embedder.get_embedding(prompt_text)
            if embedding:
                self.prompt_embeddings.append(embedding)
                self.prompt_ids_for_vectors.append(prompt_id)
    
    def find_similar_prompts(self, query_embedding: List[float], top_k: int = 3) -> List[str]:
        """Find prompts similar to query using cosine similarity."""
        # Similar logic to find_similar_tools
        
    def _create_prompt_embedding_text(self, prompt: MCPPrompt) -> str:
        """Create descriptive text for prompt embedding."""
        return f"{prompt.name} - {prompt.description} - Use case: {prompt.use_case} - Tags: {', '.join(prompt.tags)}"
```

#### **Task 1.5: Update Application Initialization**
- **Status:** Todo
- **Priority:** MEDIUM
- **Estimated Time:** 15-20 minutes
- **Dependencies:** Task 1.4
- **Description:** Update `app.py` to load prompts during startup
- **Acceptance Criteria:**
  1. Prompts loaded during application startup
  2. Vector index includes prompt embeddings
  3. Error handling for missing prompt files
  4. Startup logging for prompt loading

### **Sprint 1 Success Criteria**
- [x] MCPPrompt ontology defined and tested
- [x] Rich prompt metadata created (8-12 prompts)
- [x] InMemoryKG enhanced for prompt management
- [x] Vector indexing includes prompt embeddings
- [x] Application startup includes prompt loading
- [x] All unit tests passing
- [x] No regression in MVP 1 functionality

---

## 🚀 Sprint 2: Enhance Planner to Suggest Tool+Prompt Pairs

**Duration:** 2-3 hours  
**Priority:** HIGH  
**Dependencies:** Sprint 1 completion

### **Sprint 2 Objectives**
- Create `PlannedStep` data structure for (Tool, Prompt) pairs
- Implement intelligent prompt selection logic
- Enhance `SimplePlannerAgent` for combined suggestions
- Maintain backward compatibility with MVP 1

### **Sprint 2 Tasks**

#### **Task 2.1: Define `PlannedStep` Data Structure**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 20-30 minutes
- **Dependencies:** Task 1.1
- **Description:** Create structured representation for tool+prompt suggestions
- **Acceptance Criteria:**
  1. `PlannedStep` dataclass properly defined
  2. Serialization compatibility
  3. Optional confidence scoring
  4. Unit tests passing

**Implementation Specification:**
```python
@dataclass
class PlannedStep:
    """Represents a suggested tool with its optimal prompt."""
    tool: MCPTool
    prompt: MCPPrompt
    confidence_score: float = 0.0  # Combined tool+prompt confidence
    reasoning: str = ""  # Optional: why this combination was selected
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for JSON serialization."""
        return {
            "tool": {
                "id": self.tool.tool_id,
                "name": self.tool.name,
                "description": self.tool.description,
                "tags": self.tool.tags
            },
            "prompt": {
                "id": self.prompt.prompt_id,
                "name": self.prompt.name,
                "description": self.prompt.description,
                "template": self.prompt.template_string,
                "input_variables": self.prompt.input_variables,
                "use_case": self.prompt.use_case
            },
            "confidence_score": self.confidence_score,
            "reasoning": self.reasoning
        }
```

#### **Task 2.2: Implement Intelligent Prompt Selection**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 90-120 minutes
- **Dependencies:** Task 2.1
- **Description:** Create sophisticated prompt selection algorithm
- **Acceptance Criteria:**
  1. Multi-stage selection process (tool → prompt)
  2. Semantic ranking of prompts for selected tool
  3. Confidence scoring for combinations
  4. Fallback strategies for edge cases

**Algorithm Design:**
```python
def select_best_prompt_for_tool(self, tool: MCPTool, user_query: str, query_embedding: List[float]) -> Optional[MCPPrompt]:
    """Select the most appropriate prompt for a tool given user context."""
    
    # 1. Get all prompts for this tool
    candidate_prompts = self.kg.get_prompts_for_tool(tool.tool_id)
    if not candidate_prompts:
        return None
    
    # 2. If only one prompt, return it
    if len(candidate_prompts) == 1:
        return candidate_prompts[0]
    
    # 3. Multi-criteria selection for multiple prompts
    scored_prompts = []
    for prompt in candidate_prompts:
        score = self._calculate_prompt_relevance_score(prompt, user_query, query_embedding, tool)
        scored_prompts.append((score, prompt))
    
    # 4. Return highest scoring prompt
    scored_prompts.sort(key=lambda x: x[0], reverse=True)
    return scored_prompts[0][1]

def _calculate_prompt_relevance_score(self, prompt: MCPPrompt, user_query: str, query_embedding: List[float], tool: MCPTool) -> float:
    """Calculate relevance score for prompt given user context."""
    
    score = 0.0
    
    # Semantic similarity to user query (60% weight)
    prompt_embedding = self._get_prompt_embedding(prompt.prompt_id)
    if prompt_embedding:
        semantic_similarity = self.kg._cosine_similarity(query_embedding, prompt_embedding)
        score += semantic_similarity * 0.6
    
    # Use case matching (20% weight)
    if any(keyword in user_query.lower() for keyword in prompt.use_case.lower().split()):
        score += 0.2
    
    # Tag matching (15% weight)
    query_words = set(user_query.lower().split())
    prompt_tags = set(tag.lower() for tag in prompt.tags)
    tag_overlap = len(query_words.intersection(prompt_tags)) / max(len(prompt_tags), 1)
    score += tag_overlap * 0.15
    
    # Difficulty preference (5% weight) - prefer beginner for unclear queries
    if len(user_query.split()) < 10 and prompt.difficulty_level == "beginner":
        score += 0.05
    
    return score
```

#### **Task 2.3: Enhance SimplePlannerAgent**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 60-75 minutes
- **Dependencies:** Task 2.2
- **Description:** Implement comprehensive planning with tool+prompt suggestions
- **Acceptance Criteria:**
  1. New `plan_task_with_prompt` method
  2. Backward compatibility with existing `suggest_tools`
  3. Comprehensive error handling
  4. Performance optimization

**Enhanced Agent Implementation:**
```python
class SimplePlannerAgent:
    def plan_task_with_prompt(self, user_query: str, top_k_suggestions: int = 3) -> List[PlannedStep]:
        """Plan task by suggesting both tools and prompts."""
        
        if not user_query or not user_query.strip():
            return []
        
        query_embedding = self.embedder.get_embedding(user_query)
        if query_embedding is None:
            return []
        
        # 1. Find relevant tools
        similar_tool_ids = self.kg.find_similar_tools(query_embedding, top_k=top_k_suggestions * 2)  # Get more for filtering
        
        planned_steps: List[PlannedStep] = []
        
        for tool_id in similar_tool_ids:
            tool = self.kg.get_tool_by_id(tool_id)
            if not tool:
                continue
            
            # 2. Find best prompt for this tool
            best_prompt = self.select_best_prompt_for_tool(tool, user_query, query_embedding)
            if not best_prompt:
                continue  # Skip tools without prompts for MVP 2
            
            # 3. Calculate combined confidence
            tool_confidence = self._calculate_tool_confidence(tool, query_embedding)
            prompt_confidence = self._calculate_prompt_confidence(best_prompt, user_query, query_embedding)
            combined_confidence = (tool_confidence + prompt_confidence) / 2
            
            # 4. Create planned step
            step = PlannedStep(
                tool=tool,
                prompt=best_prompt,
                confidence_score=combined_confidence,
                reasoning=f"Selected {tool.name} with {best_prompt.name} based on semantic similarity and use case matching"
            )
            
            planned_steps.append(step)
            
            if len(planned_steps) >= top_k_suggestions:
                break
        
        # Sort by confidence and return
        planned_steps.sort(key=lambda x: x.confidence_score, reverse=True)
        return planned_steps
    
    # Maintain backward compatibility
    def suggest_tools(self, user_query: str, top_k: int = 3) -> List[MCPTool]:
        """Legacy method for backward compatibility."""
        planned_steps = self.plan_task_with_prompt(user_query, top_k)
        return [step.tool for step in planned_steps]
```

### **Sprint 2 Success Criteria**
- [x] PlannedStep data structure implemented
- [x] Intelligent prompt selection algorithm working
- [x] Enhanced SimplePlannerAgent operational
- [x] Backward compatibility maintained
- [x] Comprehensive unit tests passing
- [x] Performance benchmarks meet targets (<500ms response time)

---

## 🚀 Sprint 3: Update Gradio UI for Rich Prompt Display

**Duration:** 2-3 hours  
**Priority:** HIGH  
**Dependencies:** Sprint 2 completion

### **Sprint 3 Objectives**
- Redesign UI for tool+prompt display
- Implement rich prompt template visualization
- Add input variable guidance
- Maintain intuitive user experience

### **Sprint 3 Tasks**

#### **Task 3.1: Redesign UI Layout for Rich Content**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 45-60 minutes
- **Dependencies:** Task 2.3
- **Description:** Enhance Gradio interface for comprehensive tool+prompt display
- **Acceptance Criteria:**
  1. Clear separation of tool and prompt information
  2. Expandable/collapsible sections for detailed views
  3. Professional visual hierarchy
  4. Mobile-responsive design

**UI Design Specification:**
```python
with gr.Blocks(theme=gr.themes.Soft(), title="KGraph-MCP: AI-Powered Tool & Prompt Discovery") as app:
    gr.Markdown("# 🧠🛠️ KGraph-MCP: Intelligent Tool & Prompt Suggestions")
    gr.Markdown("Discover relevant MCP tools and get optimized prompt templates for your tasks.")
    
    with gr.Row():
        with gr.Column(scale=2):
            query_input = gr.Textbox(
                label="Describe your task",
                placeholder="e.g., 'I need to analyze customer feedback sentiment'",
                lines=3
            )
            
            with gr.Row():
                find_button = gr.Button("Find Tools & Prompts", variant="primary")
                clear_button = gr.Button("Clear", variant="secondary")
        
        with gr.Column(scale=1):
            gr.Markdown("### Example Queries:")
            gr.Markdown("""
            - "I need to analyze text sentiment"
            - "Help me summarize a long document"
            - "Generate captions for my images"
            - "Check my code for quality issues"
            """)
    
    # Results section
    results_section = gr.Group(visible=False)
    with results_section:
        gr.Markdown("## 🎯 Suggested Tools & Prompts")
        
        # Tool+Prompt cards
        suggestions_accordion = gr.Accordion("Suggestions", open=True)
        with suggestions_accordion:
            suggestions_display = gr.HTML()
    
    # Error/info display
    status_display = gr.Markdown(visible=False)
```

#### **Task 3.2: Implement Rich Content Formatting**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 60-75 minutes
- **Dependencies:** Task 3.1
- **Description:** Create beautiful HTML formatting for tool+prompt display
- **Acceptance Criteria:**
  1. Professional card-based layout
  2. Syntax highlighting for prompt templates
  3. Clear visual hierarchy
  4. Interactive elements (copy buttons, expand/collapse)

**Formatting Implementation:**
```python
def format_planned_steps_as_html(planned_steps: List[PlannedStep]) -> str:
    """Create rich HTML display for planned steps."""
    
    if not planned_steps:
        return "<p>No suggestions found. Try a different query.</p>"
    
    html_parts = []
    
    for i, step in enumerate(planned_steps, 1):
        confidence_bar = create_confidence_bar(step.confidence_score)
        
        card_html = f"""
        <div class="suggestion-card" style="border: 1px solid #e0e0e0; border-radius: 8px; padding: 16px; margin-bottom: 16px; background: white;">
            <div class="card-header" style="margin-bottom: 12px;">
                <h3 style="color: #2563eb; margin: 0; display: flex; align-items: center;">
                    <span style="background: #2563eb; color: white; border-radius: 50%; width: 24px; height: 24px; display: flex; align-items: center; justify-content: center; margin-right: 8px; font-size: 12px;">{i}</span>
                    {step.tool.name}
                </h3>
                <div class="confidence-score" style="margin-top: 4px;">
                    <small style="color: #666;">Confidence: {step.confidence_score:.1%}</small>
                    {confidence_bar}
                </div>
            </div>
            
            <div class="tool-info" style="margin-bottom: 16px;">
                <p style="margin: 0; color: #374151;"><strong>Tool:</strong> {step.tool.description}</p>
                <p style="margin: 4px 0 0 0; color: #6b7280;"><small>Tags: {', '.join(step.tool.tags)}</small></p>
            </div>
            
            <div class="prompt-info" style="border-top: 1px solid #f3f4f6; padding-top: 12px;">
                <h4 style="color: #059669; margin: 0 0 8px 0;">📝 {step.prompt.name}</h4>
                <p style="margin: 0 0 8px 0; color: #374151;">{step.prompt.description}</p>
                
                <div class="prompt-template" style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 4px; padding: 12px; margin: 8px 0;">
                    <div style="display: flex; justify-content: between; align-items: center; margin-bottom: 8px;">
                        <strong style="color: #1f2937;">Template:</strong>
                        <button onclick="navigator.clipboard.writeText('{step.prompt.template_string}')" style="background: #3b82f6; color: white; border: none; border-radius: 4px; padding: 4px 8px; font-size: 12px; cursor: pointer;">Copy</button>
                    </div>
                    <code style="display: block; white-space: pre-wrap; font-family: monospace; font-size: 14px; color: #1f2937;">{escape_html(step.prompt.template_string)}</code>
                </div>
                
                <div class="input-variables" style="margin-top: 12px;">
                    <strong style="color: #7c3aed;">Required Inputs:</strong>
                    <div style="margin-top: 4px;">
                        {format_input_variables(step.prompt.input_variables)}
                    </div>
                </div>
                
                {format_example_inputs(step.prompt.example_inputs)}
            </div>
        </div>
        """
        html_parts.append(card_html)
    
    return "".join(html_parts)

def create_confidence_bar(confidence: float) -> str:
    """Create visual confidence indicator."""
    width = int(confidence * 100)
    color = "#10b981" if confidence > 0.7 else "#f59e0b" if confidence > 0.4 else "#ef4444"
    return f'<div style="background: #e5e7eb; border-radius: 4px; height: 4px; width: 100%; margin-top: 2px;"><div style="background: {color}; height: 4px; width: {width}%; border-radius: 4px;"></div></div>'

def format_input_variables(variables: List[str]) -> str:
    """Format input variables as badges."""
    if not variables:
        return "<span style='color: #6b7280; font-style: italic;'>None</span>"
    
    badges = []
    for var in variables:
        badge = f"<span style='background: #ddd6fe; color: #5b21b6; padding: 2px 8px; border-radius: 12px; font-size: 12px; margin-right: 4px;'>{{{{ {var} }}}}</span>"
        badges.append(badge)
    
    return "".join(badges)

def format_example_inputs(examples: Dict[str, str]) -> str:
    """Format example inputs if available."""
    if not examples:
        return ""
    
    example_html = "<div style='margin-top: 8px; padding: 8px; background: #fef3c7; border-radius: 4px;'><strong style='color: #92400e;'>💡 Example:</strong><br>"
    for var, example in examples.items():
        example_html += f"<small style='color: #92400e;'><code>{var}</code>: {example}</small><br>"
    example_html += "</div>"
    
    return example_html
```

#### **Task 3.3: Update Event Handlers**
- **Status:** Todo
- **Priority:** MEDIUM
- **Estimated Time:** 30-45 minutes
- **Dependencies:** Task 3.2
- **Description:** Wire new UI components to enhanced planner
- **Acceptance Criteria:**
  1. Event handlers updated for new UI structure
  2. Error handling for display edge cases
  3. Loading states and user feedback
  4. Clear and reset functionality

### **Sprint 3 Success Criteria**
- [x] Rich UI layout implemented
- [x] Professional tool+prompt display
- [x] Interactive elements functional
- [x] Responsive design verified
- [x] Error handling comprehensive
- [x] User experience intuitive and clear

---

## 🚀 Sprint 4: Dynamic Input Fields Display (UI Polish)

**Duration:** 1-2 hours  
**Priority:** MEDIUM  
**Dependencies:** Sprint 3 completion

### **Sprint 4 Objectives**
- Enhance input variable visualization
- Add prompt template preview functionality
- Implement UI polish and refinements
- Prepare for future interactive features

### **Sprint 4 Tasks**

#### **Task 4.1: Enhanced Input Variable Display**
- **Status:** Todo
- **Priority:** MEDIUM
- **Estimated Time:** 45-60 minutes
- **Dependencies:** Task 3.3
- **Description:** Create sophisticated input variable visualization
- **Acceptance Criteria:**
  1. Interactive input variable previews
  2. Template variable highlighting
  3. Input validation indicators
  4. Placeholder/example value display

#### **Task 4.2: Prompt Template Preview**
- **Status:** Todo
- **Priority:** MEDIUM
- **Estimated Time:** 30-45 minutes
- **Dependencies:** Task 4.1
- **Description:** Add interactive prompt template preview
- **Acceptance Criteria:**
  1. Live template preview with example values
  2. Variable substitution visualization
  3. Copy-to-clipboard functionality
  4. Template customization hints

### **Sprint 4 Success Criteria**
- [x] Enhanced input visualization
- [x] Interactive template preview
- [x] Professional UI polish
- [x] Future-ready architecture for MVP 3

---

## 🚀 Sprint 5: Testing, Refinement & Documentation

**Duration:** 2-3 hours  
**Priority:** HIGH  
**Dependencies:** Sprint 4 completion

### **Sprint 5 Objectives**
- Comprehensive end-to-end testing
- Performance optimization
- Documentation updates
- Deployment preparation

### **Sprint 5 Tasks**

#### **Task 5.1: Comprehensive Testing Suite**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 60-90 minutes
- **Dependencies:** Sprint 4 completion
- **Description:** Execute thorough testing across all MVP 2 functionality
- **Acceptance Criteria:**
  1. All unit tests passing (target: 60+ tests)
  2. Integration tests for tool+prompt selection
  3. Performance benchmarks maintained
  4. Edge case handling verified

**Testing Scenarios:**
- Tool discovery with prompt selection
- Multiple prompts per tool selection logic
- UI display with various prompt complexities
- Error handling for missing prompts/tools
- Performance with larger prompt datasets

#### **Task 5.2: Performance Optimization**
- **Status:** Todo
- **Priority:** MEDIUM
- **Estimated Time:** 30-45 minutes
- **Dependencies:** Task 5.1
- **Description:** Optimize system performance for production use
- **Acceptance Criteria:**
  1. Response times <600ms (allowing for additional prompt processing)
  2. Memory usage stable
  3. Concurrent request handling
  4. Caching optimization for embeddings

#### **Task 5.3: Documentation Updates**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 60-75 minutes
- **Dependencies:** Task 5.2
- **Description:** Update all documentation for MVP 2 capabilities
- **Acceptance Criteria:**
  1. README.md updated with MVP 2 features
  2. API documentation enhanced
  3. Architecture diagrams updated
  4. Deployment guides current

#### **Task 5.4: Final Integration & CI**
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 20-30 minutes
- **Dependencies:** Task 5.3
- **Description:** Final quality assurance and CI validation
- **Acceptance Criteria:**
  1. All quality checks passing
  2. CI pipeline green
  3. Deployment readiness verified
  4. Version tagging and release notes

### **Sprint 5 Success Criteria**
- [x] Comprehensive testing completed
- [x] Performance targets met
- [x] Documentation fully updated
- [x] MVP 2 ready for production deployment

---

## 📊 MVP 2 Success Metrics

### **Functional Metrics**
- **Tool Discovery Accuracy:** >90% (maintained from MVP 1)
- **Prompt Selection Relevance:** >85% (new metric)
- **Combined Suggestion Quality:** >88% user satisfaction
- **Response Time:** <600ms (increased from MVP 1's 400ms due to additional processing)

### **Technical Metrics**
- **Test Coverage:** >85% (increased from MVP 1)
- **Code Quality:** A+ grade maintained
- **Memory Usage:** <200MB (increased modestly)
- **API Endpoints:** 8+ (expanded from MVP 1)

### **User Experience Metrics**
- **UI Usability Score:** >9/10
- **Information Clarity:** >90% users understand prompt templates
- **Task Completion Rate:** >95% successful tool+prompt discovery

---

## 🔄 Migration Strategy from MVP 1

### **Backward Compatibility**
- Maintain all existing MVP 1 API endpoints
- Support legacy `suggest_tools` method
- Gradual deprecation pathway for old interfaces

### **Data Migration**
- Existing tool data remains unchanged
- Additive prompt data without breaking changes
- Version-controlled data schema evolution

### **Deployment Strategy**
- Blue-green deployment for zero downtime
- Feature flags for gradual MVP 2 rollout
- Rollback capability to MVP 1 if needed

---

## 🚀 Future MVP 3 Preview

### **MVP 3 Vision: "Interactive Prompt Execution"**
- **User Input Fields:** Dynamic form generation for prompt variables
- **Simulated Execution:** Mock tool invocation with filled prompts
- **Result Preview:** Show what the tool would produce
- **Workflow Chaining:** Multiple tool sequences

### **Key MVP 3 Features**
- Interactive form generation from prompt templates
- Real-time prompt preview with user inputs
- Simulated tool execution and result display
- Prompt template customization and saving

---

## 📋 Resource Requirements

### **Development Resources**
- **Claude 4.0 Autonomous Project Manager:** Full-time throughout sprints
- **Cursor IDE:** Advanced AI coding assistance
- **OpenAI API:** Continued access for embeddings
- **Testing Environment:** Isolated environment for comprehensive testing

### **Infrastructure Requirements**
- **Enhanced Vector Storage:** Support for dual tool+prompt embeddings
- **Increased Memory:** ~50MB additional for prompt data
- **Extended API Rate Limits:** Additional embedding calls for prompts

### **Timeline Dependencies**
- **No External Dependencies:** Self-contained development
- **MVP 1 Stability:** Ensure MVP 1 remains operational during development
- **Incremental Deployment:** Feature-by-feature rollout capability

---

## 🎯 Risk Assessment & Mitigation

### **Technical Risks**
| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| Prompt Selection Algorithm Complexity | Medium | Medium | Start with simple heuristics, iterate |
| UI Performance with Rich Content | Low | Medium | Lazy loading, virtual scrolling |
| Embedding Storage Growth | Low | Low | Efficient storage, compression |

### **User Experience Risks**
| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| Information Overload in UI | Medium | High | Progressive disclosure, clear hierarchy |
| Prompt Template Complexity | Low | Medium | Beginner-friendly defaults, examples |
| Learning Curve for New Features | Medium | Low | Comprehensive documentation, examples |

### **Business Risks**
| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| Feature Scope Creep | Medium | Medium | Strict sprint boundaries, MVP focus |
| Development Timeline | Low | Medium | Buffer time, incremental delivery |
| MVP 1 Regression | Low | High | Comprehensive testing, backward compatibility |

---

## 🏁 Conclusion

MVP 2 represents a significant evolution of the KGraph-MCP system, transforming it from a discovery tool into an actionable task planning platform. By adding sophisticated prompt template management and intelligent selection algorithms, MVP 2 bridges the gap between "what tool to use" and "how to use it effectively."

The 5-sprint plan provides a structured approach to delivering this enhanced functionality while maintaining the quality and reliability established in MVP 1. Each sprint builds incrementally on the previous work, ensuring stable progress and early value delivery.

**Key Success Factors:**
1. **Building on Solid Foundation:** MVP 1's success provides proven architecture
2. **Incremental Development:** Each sprint delivers standalone value
3. **Quality First:** Comprehensive testing and documentation throughout
4. **User-Centric Design:** Focus on clear, actionable interface improvements

**Expected Outcomes:**
- Enhanced user productivity through actionable suggestions
- Professional-grade prompt engineering automation
- Scalable architecture for future MVP iterations
- Continued innovation in AI-powered development tools

---

**MVP 2 Start Date:** TBD (Post-MVP 1 Deployment)  
**Target Completion:** 5 sprints (10-15 hours total development time)  
**Success Probability:** HIGH (building on MVP 1 foundation)  
**Innovation Level:** SIGNIFICANT (first-of-kind tool+prompt discovery system)

*This comprehensive plan provides the roadmap for transforming KGraph-MCP into a truly actionable AI-powered development assistant.*