Review Analysis
Command
/review-analysis {ASIN} {SITE}
Goal
Turn review data into product decisions: what users love, why users complain, and what should be fixed first.
Credential Preflight
Before any data call, read references/runtime-credential-preflight.md when it is available.
- If SellerSprite MCP is not configured, unavailable, or returns an authentication error, ask the user for the SellerSprite MCP API key before review analysis.
- If Gemini/GLM validation is part of the requested output and model keys are missing, ask for the missing keys before validation.
- Do not continue with mock reviews or fabricated VOC conclusions.
- Do not write provided keys into reports, raw files, examples, or the open-source repository.
Required Reference
Read references/sellersprite-mcp-api.md before execution when it is available.
Normalize GB to SellerSprite MCP UK before tool calls.
Data Calls
asin_detail: product context.review with starList: [4,5]: positive reviews. Use pagination and record sample size.review with starList: [1,2,3]: negative reviews. Use pagination and record sample size.- Optional
review filters: typeList for image/video/VP/vine reviews, startTimestamp, endTimestamp, page, size, and returnFields.
asin_sales_trend and traffic_keyword: optional context for whether VOC issues correlate with sales or keyword expectation gaps.keepa_info: optional price/rank history context when available.
Save raw or compact review responses before summarization. Do not quote large review dumps in public reports; aggregate themes and keep customer-identifying data out.
Analysis Dimensions
| Dimension |
What to Identify |
| Product defect |
broken parts, durability, failure |
| Design/function gap |
usability, missing function, poor fit |
| Material/appearance |
smell, scratches, color, texture |
| Description mismatch |
expectation gap from title/images/bullets |
| Service/logistics |
used item, missing parts, late delivery |
| Return risk |
high-friction issues likely to cause returns |
Only mark an issue as a confirmed pain point when it appears in MCP review evidence or an existing user-provided source. Otherwise label it as a hypothesis.
Output
Save under:
review-analysis-reports/{ASIN}_{SITE}_{YYYYMMDD}/report.md
Do not publish raw review dumps or customer-identifying data.
1---2name: review-analysis3description: Amazon review and VOC analysis using SellerSprite MCP. Use when the user runs `/review-analysis {ASIN} {SITE}` to analyze positive and negative reviews, identify product pain points, listing mismatch, service risks, and product improvement actions.4---56# Review Analysis78## Command910```text11/review-analysis {ASIN} {SITE}12```1314## Goal1516Turn review data into product decisions: what users love, why users complain, and what should be fixed first.1718## Credential Preflight1920Before any data call, read `references/runtime-credential-preflight.md` when it is available.2122- If SellerSprite MCP is not configured, unavailable, or returns an authentication error, ask the user for the SellerSprite MCP API key before review analysis.23- If Gemini/GLM validation is part of the requested output and model keys are missing, ask for the missing keys before validation.24- Do not continue with mock reviews or fabricated VOC conclusions.25- Do not write provided keys into reports, raw files, examples, or the open-source repository.2627## Required Reference2829Read `references/sellersprite-mcp-api.md` before execution when it is available.30Normalize `GB` to SellerSprite MCP `UK` before tool calls.3132## Data Calls3334- `asin_detail`: product context.35- `review` with `starList: [4,5]`: positive reviews. Use pagination and record sample size.36- `review` with `starList: [1,2,3]`: negative reviews. Use pagination and record sample size.37- Optional `review` filters: `typeList` for image/video/VP/vine reviews, `startTimestamp`, `endTimestamp`, `page`, `size`, and `returnFields`.38- `asin_sales_trend` and `traffic_keyword`: optional context for whether VOC issues correlate with sales or keyword expectation gaps.39- `keepa_info`: optional price/rank history context when available.4041Save raw or compact review responses before summarization. Do not quote large review dumps in public reports; aggregate themes and keep customer-identifying data out.4243## Analysis Dimensions4445| Dimension | What to Identify |46|---|---|47| Product defect | broken parts, durability, failure |48| Design/function gap | usability, missing function, poor fit |49| Material/appearance | smell, scratches, color, texture |50| Description mismatch | expectation gap from title/images/bullets |51| Service/logistics | used item, missing parts, late delivery |52| Return risk | high-friction issues likely to cause returns |5354Only mark an issue as a confirmed pain point when it appears in MCP review evidence or an existing user-provided source. Otherwise label it as a hypothesis.5556## Output5758Save under:5960```text61review-analysis-reports/{ASIN}_{SITE}_{YYYYMMDD}/report.md62```6364Do not publish raw review dumps or customer-identifying data.
Ethan-paseya