microsoft / microsoft/sql-ai-promptathon
Mission: Product-Quality Risk Investigation for Zava — SQL + MCP Agentic Workflow
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- Dominant language
- Shell
- Stars
- 49
- Forks
- 132
- PR merge metrics
- No merged PRs in 30d
Description
Mission/open goal Description
Investigate product-quality risk for Zava and produce an evidence-backed recommendation by combining SQL-based quantitative analysis with qualitative insights from documentation, support chats, and vector similarity search.
Harness and model
GitHub Copilot, VS code, Jupyter notebook,
Turn-by-turn journey
A structured agentic workflow was used:
- Enumerated SQL MCP entities and confirmed schema.
- Executed aggregation SQL queries using
sqlcmdagainstPromptathonDb. - Inspected
DocsandSupportChatsfor qualitative evidence. - Ran MCP custom tool
FindSimilarDocsByDocIdto perform vector similarity search. - Consolidated quantitative and qualitative evidence into an executive brief.
Prompts and agent interactions
- “List available SQL MCP entities.” → MCP
describe_entities - “Show top product categories by revenue.” → SQL via
sqlcmd - “Which SKUs have most support tickets and low satisfaction?” → SQL aggregation on
SupportTickets - “Show docs and run vector similarity for DocId=39.” → MCP
find_similar_docs_by_doc_id
MCP tool calls used
describe_entities— listed: Customer, Doc, Employee, FindSimilarDocsByDocId, Product, SalesOrder, SalesOrderLine, SupportChat, SupportTicketread_records— attempted, but direct SQL queries were used for full controlfind_similar_docs_by_doc_id— executed withDocId=39, TopN=5
🧩 Artifacts
- Notebook:
artifacts/ZCPTM_issue_notebook.ipynb
(contains queries, outputs, and executive brief) - Submission journey:
submission_journey.md - Executive brief (embedded in notebook)
https://github.com/microsoft/sql-ai-promptathon/pull/28
Completion
- Yes, the agent completed the mission or goal.
- No, the agent did not complete the mission or goal.
Bonus work
No response
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with artifacts/ZCPTM_issue_notebook.ipynb and submission_journey.md. Review the sqlcmd queries, MCP interactions, vector search for DocId=39, and embedded executive brief. Done means the notebook contains the quantitative and qualitative evidence and an evidence-backed recommendation; PR #28 indicates this work has already moved elsewhere.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- github, jupyter-notebook, sql, vscode
- Domain
- data, database, documentation
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 25/100