customerFeedback_getBrowseNodeReviewTopics
The 10 most positive and 10 most negative review topics of a whole browse node (US, UK, FR, IT, DE, ES, JP only).
readAmazon Seller (SP-API)sp.customerFeedback_2024-06-01
Call it TWICE per node — sortBy MENTIONS (recent 6 months: what customers mention most) and sortBy STAR_RATING_IMPACT (~18 months: what moves the star rating); the two datasets are complementary. Then customerFeedback_getBrowseNodeReviewTrends. Amazon SP-API · getBrowseNodeReviewTopics · GET /customerFeedback/2024-06-01/browseNodes/{browseNodeId}/reviews/topics
Call it
In an AI client (MCP)
{
"name": "customerFeedback_getBrowseNodeReviewTopics",
"arguments": {
"store": "myStore-US",
"browseNodeId": "<browseNodeId>",
"sortBy": "MENTIONS"
}
}From code (Tool API)
curl -X POST https://api.sellersheetai.com/api/v1/tools/customerFeedback_getBrowseNodeReviewTopics \
-H "Authorization: Bearer $SELLERSHEET_API_KEY" \
-H "Content-Type: application/json" \
-d '{"store":"myStore-US","browseNodeId":"<browseNodeId>","sortBy":"MENTIONS"}'Early access — the Tool API is open on request, contact us.
Arguments
| Name | Type | Description |
|---|---|---|
storerequired | string | the store ref `<store>-<CC>` (name-country, e.g. 'myStore-US') — one of get_user_context's store_refs, verbatim. |
browseNodeIdrequired | string | from customerFeedback_getItemBrowseNode (e.g. '10671064011'). |
sortByrequired | string | the metric the topics are ranked by — one per call, each described above. Allowed: MENTIONS | STAR_RATING_IMPACT.MENTIONSSTAR_RATING_IMPACT |
summarize | boolean | true returns the slim shape below (~93 % smaller) — use it in AI workflows; default false returns Amazon's raw payload.Default: false |
Result
Every tool answers {notification, data, human_action}; data carries:
data.reviewTopics— category-level — no asinMetrics or childAsinMetrics (item-only).data.reviewTopics.Raw— {browseNodeId, displayName, marketplaceId, countryCode, dateRange: {startDate, endDate}, topics: {positiveTopics: [Topic], negativeTopics: [Topic]}} (up to 10 each), Topic = {topic, browseNodeMetrics: {occurrencePercentage: {allProducts, topTwentyFivePercentProducts}, starRatingImpact: {allProducts, topTwentyFivePercentProducts}}, reviewSnippets: [str] (up to 3), subtopics: [{subtopic, metrics: {occurrencePercentage}, reviewSnippets}] (up to 5)} — allProducts is the whole category, topTwentyFivePercentProducts the best-selling quartile (the leaders). Slim: {topics: {positiveTopics: [SlimTopic], negativeTopics: [SlimTopic]}}, SlimTopic = {topic, browseNodeMetrics, reviewSnippets (top 3), subtopics (top 3, {subtopic, occurrencePercentage}), leaders_pct_ratio (topTwentyFivePercentProducts ÷ allProducts, null when either is missing)} — on a NEGATIVE topic a ratio above 1 means the category leaders still suffer the issue: an unsolved category-wide gap.
Gotchas
- Some STAR_RATING_IMPACT topics have no occurrencePercentage, and Amazon may return an unresolved '$…' placeholder string there — treat a non-numeric value as absent. The browse-node subtopic metric is occurrencePercentage only (numberOfMentions is item-level, on customerFeedback_getItemReviewTopics).