Mission 1: ZCPTM-SS-M-BW product-quality investigation

.devcontainer/devcontainer-lock.json

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artifacts/submission_journey.md

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+# Promptathon Mission 1 — Submission Journey
+
+## 1) 目的
+Investigate product-quality risk for Zava and produce evidence-backed recommendation.
+
+## 2) High-level workflow
+- Use SQL MCP to enumerate entities and confirm schema.
+- Use `sqlcmd` to run aggregation SQL against `PromptathonDb`.
+- Inspect `Docs` and `SupportChats` for qualitative evidence.
+- Run the MCP custom tool `FindSimilarDocsByDocId` for vector similarity.
+- Consolidate quantitative + qualitative evidence and prepare executive brief.
+
+## 3) Prompts and agent interactions (high-level)
+- "List available SQL MCP entities." → used the MCP `describe_entities` call.
+- "Show top product categories by revenue." → executed 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." → executed `find_similar_docs_by_doc_id`.
+
+## 4) MCP tool calls used
+- `describe_entities` — to list `Customer`, `Doc`, `Employee`, `FindSimilarDocsByDocId`, `Product`, `SalesOrder`, `SalesOrderLine`, `SupportChat`, `SupportTicket`.
+- `read_records` attempts were made but the working path used direct `sqlcmd` queries for full control and to capture raw outputs.
+- `find_similar_docs_by_doc_id` executed with `DocId=39, TopN=5` to validate similarity clustering.
+
+## 5) SQL queries executed (examples)
+- Category revenue:
+
+```
+SELECT TOP 15 ProductCategory, SUM(LineTotal) AS Revenue, SUM(Quantity) AS QuantitySold, COUNT(*) AS OrderLines
+FROM dbo.SalesOrderLines
+GROUP BY ProductCategory
+ORDER BY SUM(LineTotal) DESC;
+```
+
+- Top SKUs:
+
+```
+SELECT TOP 20 SKU, ProductName, ProductCategory, SUM(LineTotal) AS Revenue, SUM(Quantity) AS QuantitySold, COUNT(DISTINCT OrderId) AS Orders
+FROM dbo.SalesOrderLines
+GROUP BY SKU, ProductName, ProductCategory
+ORDER BY SUM(LineTotal) DESC;
+```
+
+- Support tickets by SKU:
+
+```
+SELECT COALESCE(RelatedSKU,'UNKNOWN') AS RelatedSKU, COUNT(*) AS TicketCount, AVG(CAST(SatisfactionScore AS FLOAT)) AS AvgSat,
+SUM(CASE WHEN Priority='High' THEN 1 ELSE 0 END) AS HighPriority,
+SUM(CASE WHEN Status='Open' THEN 1 ELSE 0 END) AS OpenCount
+FROM dbo.SupportTickets
+GROUP BY COALESCE(RelatedSKU,'UNKNOWN')
+ORDER BY COUNT(*) DESC;
+```
+
+- Docs for SKU:
+
+```
+SELECT DocId, SourceType, SourceId, RelatedSKU, Title, LEFT(Body,200) AS BodySnippet, TagsJson
+FROM dbo.Docs
+WHERE RelatedSKU='ZCPTM-SS-M-BW'
+ORDER BY DocId;
+```
+
+- Vector search (MCP stored procedure):
+
+```
+EXEC dbo.FindSimilarDocsByDocId @DocId = 39, @TopN = 5;
+```
+
+## 6) Errors, fixes, and notable troubleshooting
+- Error: `sqlcmd` SSL/TLS certificate verify failed (self-signed certificate). Fix: add `-C` (Trust Server Certificate) option to `sqlcmd` and to the Python helper's trust flag.
+- Attempted MCP `read_records` calls with certain entity names returned `EntityNotFound` errors in that path, so direct `sqlcmd` queries were used for robust, copyable outputs.
+- Confirmed DB name `PromptathonDb` and table list in `INFORMATION_SCHEMA.TABLES` before running aggregations.
+
+## 7) Key findings (final)
+- Risk cluster: `ZCPTM-SS-M-BW` (Premium Short Sleeve Men's Top). Multiple lines of evidence:
+  - Commercial: SKU is among top revenue-contributing SKUs (Revenue ~ $19.8k, QuantitySold=228, Orders=105).
+  - Support: 9 support tickets for this SKU, average satisfaction ≈ 1.67 (low), includes high/critical priorities.
+  - Qualitative: Multiple reviews and support chats in English, Spanish, French describing failure of smart fabric connectivity after washing.
+  - Vector search: Representative negative review (DocId=39) yields similar negative reviews in top results.
+
+## 8) Artifacts
+- Notebook: `artifacts/ZCPTM_issue_notebook.ipynb` (contains queries, captured outputs, and executive brief).
+
+## 9) Suggested submission contents
+- The notebook artifact.
+- This `submission_journey.md` describing prompts, tool calls, SQL commands, errors/fixes, and findings.
+- A short executive brief (already embedded in the notebook). 
+
+
+## 10) Next steps
+- (Optional) Add charts and failure-rate computations by matching shipped quantities (requires POS/shipments data).
+- Finalize GitHub issue with the above attachments and a short narrative of the journey and decisions made.

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