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 sqlcmd against PromptathonDb.
  • Inspected Docs and SupportChats for qualitative evidence.
  • Ran MCP custom tool FindSimilarDocsByDocId to 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, SupportTicket
  • read_records — attempted, but direct SQL queries were used for full control
  • find_similar_docs_by_doc_id — executed with DocId=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

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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