# KGraph-MCP MVP Tasks - Complete Extraction from full_plan.md
# Format: MVP|Sprint|Name|Objective
# This file contains ALL tasks from the comprehensive 5-MVP plan

# MVP 1: KG-Powered Tool Suggester (Day 1-2)
1|1|KG_Structure|Create in-memory Knowledge Graph structure for storing MCP Tool metadata and embeddings
1|1|Tool_Data_Loading|Load initial MCP Tools from JSON into the in-memory KG with structured and vector storage
1|1|Embedder_Service|Implement embedding generation service using LLM APIs for tool descriptions
1|1|Semantic_Search|Implement cosine similarity search for finding relevant tools based on query embeddings
1|1|Simple_Planner|Create SimplePlannerAgent that embeds queries and searches for relevant tools
1|1|Basic_Gradio_UI|Build basic Gradio interface with query input and tool suggestions display

# MVP 2: KG Suggests Actionable Tool with Prompt Template (Day 2-3)
2|1|Prompt_Ontology|Define MCPPrompt dataclass and extend KG to store prompt templates
2|1|Prompt_Data_Loading|Load initial prompts from JSON and link them to tools in the KG
2|1|Enhanced_Planner|Enhance planner to select both tools and associated prompts based on query
2|1|UI_Prompt_Display|Update Gradio UI to display selected prompt templates with placeholders
2|1|PlannedStep_Structure|Create PlannedStep dataclass to represent tool+prompt selections

# MVP 3: Interactive Prompt Filling & Simulated Execution (Day 3-4)
# Sprint 1: Dynamic UI Generation
3|1|Dynamic_Input_Fields|Implement dynamic Gradio input field generation based on prompt variables
3|1|Input_Collection|Create mechanism to collect user inputs from dynamic fields
3|1|UI_State_Management|Implement UI state transitions for planning and execution phases

# Sprint 2: User Input Collection & Executor Foundation
3|2|Input_Validation|Add input validation for dynamic fields based on prompt requirements
3|2|Stub_Executor|Create StubExecutorAgent with basic simulate_execution method
3|2|Execute_Button|Add Execute Plan button and wire it to input collection
3|2|Handler_Integration|Implement handle_execute_plan handler in app.py

# Sprint 3: Integrate Executor & Display Simulated Results
3|3|Tool_Specific_Mocks|Enhance StubExecutorAgent to return tool-specific mock responses based on tool_id
3|3|Executor_Integration|Integrate StubExecutorAgent into app.py workflow and global service initialization
3|3|Result_Display|Format and display simulated execution results in UI with proper Markdown

# Sprint 4: Refine Simulated Outputs & UI Polish
3|4|Input_Aware_Mocks|Make mock responses sensitive to user inputs with context-aware outputs
3|4|Error_Simulation|Add simulated error states for edge cases and input validation failures
3|4|UI_Polish|Improve execution result display with better formatting and status indicators
3|4|Button_Feedback|Add execute button state management and loading indicators

# Sprint 5: MVP3 Finalization & Documentation
3|5|E2E_Testing|Comprehensive end-to-end regression testing of MVP1-3 features integration
3|5|Documentation_Update|Update all project READMEs and documentation for MVP3 functionality
3|5|Code_Cleanup|Final code review, cleanup, and deployment preparation for MVP3
3|5|Quality_Assurance|Run all quality checks, CI validation, and performance testing

# MVP 4: Real MCP Tool Execution via Gradio Server (Day 4-5)
# Sprint 1: First Track 1 MCP Server (Summarizer)
4|1|Summarizer_Project_Init|Create new separate project directory for MCP Summarizer Tool
4|1|Summarizer_Environment|Set up Python environment, dependencies, and project structure
4|1|Summarizer_Logic|Implement text summarization using Hugging Face Inference API
4|1|Summarizer_Gradio_MCP|Create Gradio UI and expose as MCP server with /mcp endpoint
4|1|Summarizer_Testing|Unit test summarization logic with mocked API calls
4|1|Summarizer_Deploy|Deploy Summarizer to Hugging Face Space with documentation

# Sprint 2: Second Track 1 MCP Server (Sentiment Analyzer)
4|2|Sentiment_Project_Init|Create new separate project directory for MCP Sentiment Analyzer Tool
4|2|Sentiment_Environment|Set up Python environment, dependencies, and project structure
4|2|Sentiment_Logic|Implement sentiment analysis using Hugging Face Inference API
4|2|Sentiment_Gradio_MCP|Create Gradio UI and expose as MCP server with /mcp endpoint
4|2|Sentiment_Testing|Unit test sentiment analysis logic with mocked API calls
4|2|Sentiment_Deploy|Deploy Sentiment Analyzer to Hugging Face Space with documentation

# Sprint 3: Update Main KG & Executor for Real MCP Calls
4|3|MCPTool_Ontology_Update|Add execution_type field to MCPTool dataclass to distinguish live vs simulated
4|3|Tools_JSON_Update|Update initial_tools.json with live MCP server URLs and execution types
4|3|McpExecutorAgent_Create|Refactor StubExecutorAgent to McpExecutorAgent for real HTTP calls
4|3|HTTP_Client_Logic|Implement HTTP POST requests to live MCP servers with proper payload construction
4|3|Error_Handling_Network|Add robust error handling for network failures, timeouts, and parsing errors
4|3|App_Integration_Update|Update app.py to use McpExecutorAgent instead of StubExecutorAgent

# Sprint 4: End-to-End Testing with Live MCP Tools & UI Polish
4|4|Live_Integration_Testing|Test complete flow from query to real MCP execution with both tools
4|4|UI_Live_Indicators|Differentiate live vs simulated results in UI with visual indicators
4|4|Error_Display_Enhancement|Improve display of live MCP call errors with user-friendly messaging
4|4|Performance_Optimization|Optimize performance for real tool execution and error recovery

# Sprint 5: Final Documentation, Videos & Submission Prep
4|5|Track1_Documentation_Complete|Update all READMEs for Track 1 tools and main project MVP4 integration
4|5|Demo_Videos_Recording|Record demo videos for main Track 3 demo and individual Track 1 tools
4|5|HF_Spaces_Finalization|Finalize all Hugging Face Spaces with proper secrets, tags, and metadata
4|5|Submission_Artifacts_Prep|Prepare all hackathon submission artifacts and final CI validation

# MVP 5: KG-Informed Model Preferences for Sampling (Day 5-6)
# Sprint 1: Enhance KG Ontology & Data for Model Preferences
5|1|MCPPrompt_Sampling_Fields|Add sampling preference fields to MCPPrompt dataclass (model hints, priorities)
5|1|Sampling_Data_Population|Update initial_prompts.json with diverse sampling preference examples
5|1|KG_Loading_Enhancement|Update InMemoryKG to load new sampling preference fields correctly
5|1|Ontology_Testing|Test MCPPrompt instantiation and KG loading with new sampling fields

# Sprint 2: Planner/Agent Logic to Construct Conceptual Sampling Request
5|2|Sampling_Request_Construction|Add method to SimplePlannerAgent for constructing MCP sampling/createMessage params
5|2|Model_Preferences_Dynamic|Populate modelPreferences dynamically from KG data (hints, priorities)
5|2|Sampling_Parameters_Logic|Implement logic for systemPrompt, includeContext, temperature, maxTokens
5|2|Sampling_Agent_Testing|Unit test sampling request construction with various PlannedStep configurations

# Sprint 3: Gradio UI for Triggering and Displaying Conceptual Sampling Request
5|3|Sampling_UI_Components|Add UI elements for sampling request (button, JSON display, accordion)
5|3|Sampling_Request_Handler|Implement handle_construct_sampling_request Gradio handler
5|3|Sampling_Button_Integration|Wire construct sampling button to backend handler with proper outputs
5|3|Sampling_UI_Testing|Test sampling request generation and display in UI

# Sprint 4: (Optional Stretch) Simulate LLM Call for Sampling Refinement
5|4|LLM_Refinement_Method|Add LLM call logic for sampling refinement suggestions
5|4|Chat_Completion_Service|Extend EmbeddingService with get_chat_completion for meta-prompting
5|4|Refinement_UI_Display|Update UI to display LLM-generated sampling refinement suggestions
5|4|LLM_Integration_Testing|Test LLM refinement call with mocked responses

# Sprint 5: Final Testing, Documentation Update & Innovation Showcase
5|5|Sampling_Features_Testing|Manual E2E testing of conceptual sampling features
5|5|Innovation_Documentation|Update READMEs to highlight innovative MCP usage for judging criteria
5|5|Final_Submission_Complete|Final code review, cleanup, video updates, and hackathon submission prep
5|5|MVP5_Quality_Assurance|All checks pass, CI green, and project ready for final evaluation