docs/progress/sprint5_plan.md

Sprint 5 (MVP 1): Final Testing, Documentation, and Deployment Prep

Sprint Overview

• Goal: Thoroughly test the completed MVP 1, write comprehensive documentation, ensure all code is clean and CI is passing, and prepare the application for deployment to a Hugging Face Space.
• Duration: Estimated 2-4 hours (flexible within Hackathon Day 2).
• Core Primitives Focused On: Finalizing the demonstration of Tool discovery.
• Key Artifacts by End of Sprint:
  • A robust, well-tested app.py for MVP 1
  • A comprehensive README.md in the GitHub repository
  • A README.md specifically formatted for the Hugging Face Space
  • All necessary configuration files for Hugging Face Space deployment
  • Confirmation that the app runs correctly when launched as it would be on a Space
  • Final CI pass

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Task List

Task 5.1: Comprehensive End-to-End Testing & Bug Fixing

• Task ID: 20

• Status: Todo

• Dependencies: Task 19 (Task 4.4: Update Dependencies & Run All Checks)

• Description:

  1. Perform rigorous testing of the Gradio application (app.py).
     • Test with a wider variety of queries, including longer ones, ambiguous ones, and some with typos
     • Test edge cases for the InMemoryKG (e.g., malformed data/initial_tools.json)
     • Test UI responsiveness and browser console for JavaScript errors
     • Test in a clean environment to ensure all dependencies are captured
  2. Identify and fix any bugs or unexpected behaviors found during testing.

• Acceptance Criteria:

  1. Application is stable and handles a variety of inputs gracefully
  2. Known critical bugs are fixed
  3. User experience is smooth for the defined MVP1 scope

• Test Scenarios:

  • Strong Match Query: "I need sentiment analysis for customer feedback"
  • Ambiguous Query: "Help me with text processing"
  • Generic Query: "I need a tool"
  • Unrelated Query: "How do I cook pasta?"
  • Typo Query: "I need sentimnt anaylsis"

• Edge Cases to Test:

  • Missing data/initial_tools.json file
  • Empty or malformed JSON data
  • Missing or invalid API keys
  • Network connectivity issues
  • Large input queries
  • Special characters and Unicode

Task 5.2: Finalize Project README.md for GitHub

• Task ID: 21

• Status: Todo

• Dependencies: Task 20 completion

• Description: Review and expand the README.md to accurately reflect MVP 1's functionality and align with the hackathon report. Include:

  • Clear Project Title & Hackathon Context
  • Concise Description of MVP 1
  • "How KGraph-MCP (MVP1) Works" section (brief architecture, data flow)
  • "Our Development Process" (mentioning Cursor, Claude, uv, iterative sprints)
  • "Technologies Used" (Python, Gradio, uv, specific LLM for embeddings)
  • "How to Run Locally" (clear uv-based instructions)
  • "Sponsor Technologies Utilized" (specifically which API credits were used)
  • "Future Vision / Next Steps" (briefly what MVP2+ and the larger project aim for)
  • Link to the (upcoming) Hugging Face Space

• Acceptance Criteria:

  1. README.md is comprehensive, accurate for MVP1, and well-formatted
  2. All specified sections are present and clearly articulated
  3. Instructions are tested and verified to work
  4. Professional presentation suitable for hackathon judges

Task 5.3: Prepare README.md for Hugging Face Space

• Task ID: 22

• Status: Todo

• Dependencies: Task 21 completion

• Description: Create or adapt a README.md specifically for the Hugging Face Space hosting the Gradio app. This involves:

  • Adding the required hackathon track tag (e.g., agent-demo-track)
  • Ensuring specific metadata Hugging Face Spaces uses is correctly specified
  • Making sure "How to Use" is very clear for someone just landing on the Space
  • Linking back to the GitHub repository

• Required YAML Frontmatter: ```yaml

  title: KG-Powered MCP Tool Suggester (MVP1) emoji: 🧠🛠️ colorFrom: indigo colorTo: blue sdk: gradio sdk_version: 5.33.0 python_version: 3.11.8 app_file: app.py pinned: false hf_oauth: false hf_storage: false hf_cookies: false datasets: [] models: [] tags: - "agents-mcp-hackathon" - "agent-demo-track" - "mcp" - "knowledge-graph" - "ai-agents" - "gradio"

• Acceptance Criteria:

  1. A Space-ready README.md is prepared with proper YAML frontmatter
  2. Includes necessary Hugging Face metadata and hackathon tags
  3. Clear, concise description suitable for Space visitors
  4. Easy-to-follow usage instructions

Task 5.4: Final Code Review & Cleanup

• Task ID: 23

• Status: Todo

• Dependencies: Task 22 completion

• Description:

  1. Review all Python code written for MVP1 (app.py, kg_services/, agents/)
  2. Remove any unused imports, commented-out dead code, or unnecessary print statements
  3. Ensure docstrings and comments are clear and up-to-date
  4. Double-check error handling, especially around API calls and file loading

• Files to Review:

  • app.py
  • kg_services/ontology.py
  • kg_services/knowledge_graph.py
  • kg_services/embedder.py
  • agents/planner.py

• Review Checklist:

  • Remove unused imports
  • Remove commented-out dead code
  • Keep essential print statements for debugging
  • Verify docstrings are adequate and current
  • Check try-except blocks for proper error handling
  • Ensure consistent code style
  • Verify type hints are present and accurate

• Acceptance Criteria:

  1. Codebase is clean, well-commented, and free of dead code
  2. All functions and classes have adequate docstrings
  3. Error handling is comprehensive and appropriate

Task 5.5: Prepare for Deployment to Hugging Face Space

• Task ID: 24

• Status: Todo

• Dependencies: Task 23 completion

• Description:

  1. Ensure requirements.txt is minimal and contains all necessary runtime dependencies
  2. Ensure API keys needed by EmbeddingService are planned to be set as Secrets in the Hugging Face Space settings
  3. Test running the app locally using the command that Hugging Face Spaces would use
  4. Create deployment documentation and secret management guide

• Requirements.txt Dependencies:

  • gradio>=5.33.0
  • fastapi>=0.115.0
  • uvicorn>=0.31.0
  • openai>=1.8.0
  • numpy>=1.21.0
  • pydantic>=2.11.0
  • python-dotenv>=1.0.0 (for local development)

• Secret Management Plan:

  • OPENAI_API_KEY must be set as a Secret in HF Space settings
  • Update .env.example to reflect required environment variables
  • Document the secret setup process for deployment

• Acceptance Criteria:

  1. requirements.txt is finalized for deployment
  2. Plan for managing API key secrets on Hugging Face Spaces is clear
  3. App is confirmed to run with a simple python app.py
  4. Deployment documentation is complete

Task 5.6: Final Checks & CI Pass

• Task ID: 25

• Status: Todo

• Dependencies: Task 24 completion

• Description:

  1. Run just install one last time
  2. Run just lint, just format, just type-check, just test locally
  3. Commit all final changes
  4. Push to GitHub and ensure the CI pipeline passes cleanly

• Final Checklist:

  • just install - Dependencies updated
  • just lint - Code linting passed
  • just format - Code formatting applied
  • just type-check - Type checking passed
  • just test - All tests passing
  • All changes committed with conventional commit messages
  • GitHub CI pipeline green
  • MVP1 ready for deployment and submission

• Acceptance Criteria:

  1. All local checks pass
  2. All code is committed and pushed
  3. GitHub Actions CI pipeline is green
  4. MVP1 is considered "feature complete" and ready for deployment/submission

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Sprint 5 Success Criteria

By the end of Sprint 5, we will have:

✅ Production-Ready MVP 1

• Thoroughly tested application with comprehensive edge case coverage
• Clean, well-documented codebase ready for production
• Robust error handling and user experience
• All quality gates passing

✅ Complete Documentation

• Professional GitHub README with full project details
• Hugging Face Space README with proper metadata and tags
• Clear deployment and usage instructions
• Comprehensive development process documentation

✅ Deployment Readiness

• Finalized requirements.txt for Hugging Face Spaces
• API key management strategy documented
• Local deployment verified and tested
• CI/CD pipeline green and stable

✅ Hackathon Submission Ready

• Application meets all Track 3 requirements
• Professional presentation materials ready
• Demo scenarios tested and verified
• Future roadmap clearly articulated

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End of Sprint 5 & MVP 1 Review

What's Done:

• Hackathon MVP 1 ("KG-Powered Tool Suggester") is complete, tested, and documented
• The Gradio app demonstrates semantic search for MCP tools based on an in-memory KG
• The project is ready for deployment to a Hugging Face Space and submission for Track 3
• The foundation is set for subsequent, more complex MVPs

What's Next (Post-MVP1 / Hackathon MVP 2):

• Deploy MVP1 to a Hugging Face Space
• Start planning/implementing MVP2 ("KG Suggests Actionable Tool with Prompt Template")
• Gather feedback from hackathon judges and community
• Iterate on the architecture based on real-world usage

Key Achievements:

• ✅ Fully functional semantic tool discovery system
• ✅ Professional-grade documentation and presentation
• ✅ Production-ready deployment configuration
• ✅ Comprehensive testing and quality assurance
• ✅ Clear roadmap for future development

This sprint ensures that MVP1 is not just functional but also well-packaged, documented, and ready for the world (and hackathon judges) to see!

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Sprint Priority: HIGH - This is the final sprint before hackathon submission
Risk Level: LOW - Building on solid foundation from Sprint 4
Confidence: HIGH - Clear tasks with well-defined outcomes