Pangolinfo Review Mining MCP

io.github.Pangolin-spg由 GitHub 账号 Pangolin-spg 发布v0.1.2更新于 Nov 3, 2026

Amazon review mining for AI agents — complaint detection, sentiment trends, complaint topics.

发布方自己的服务:Review Expert

已验证STDIO仅桌面数据与分析商业与电商

概览

AI 生成的概览

针对单个 ASIN 挖掘亚马逊商品评论:投诉主题、情绪趋势和综合评论挖掘报告,数据来自 Pangolinfo API。

可以做什么
  • 分页获取某个 ASIN 的亚马逊评论,并原样报告计费信息
  • 从 1-3 星差评中提取投诉信号,标注主题及触发关键词
  • 汇总评分分布、平均星级、好评与差评占比以及逐月情绪趋势
  • 对投诉主题排序,给出数量、占比和示例评论标题
  • 生成单个 ASIN 的综合报告,包含总量、趋势、热门主题和最有帮助的投诉
适用场景
当你需要了解某款亚马逊商品的买家在抱怨什么、以及这些抱怨随时间如何变化时使用,而不是用于商品搜索或类目数据。它是 Pangolinfo 通用亚马逊数据服务器的纯评论补充工具。
运行要求
需要 Node.js 18 或更高版本,以本地 stdio 进程运行。实时调用需要 PANGOLINFO_API_KEY 中的 Pangolinfo API 密钥;未设置时服务器仍可启动,但工具调用会失败。需要访问 Pangolinfo 托管 MCP 端点的网络,可用 PANGOLINFO_MCP_URL 覆盖。可通过 PANGOLINFO_REVIEW_MINING_MOCK 启用模拟模式。
安装前请注意
实时调用会消耗 Pangolinfo 点数(文档为每页 5 点,上游描述写 10 点;免费额度为每月 60 点),综合报告会发起两次抓取,费用翻倍。API 密钥以 bearer 令牌形式发送给 Pangolinfo 托管服务器。投诉标注是英文关键词启发式方法,非英文评论匹配不足;结果应视为信号而非已核实的事实。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Pangolinfo Review Mining MCP,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

Pangolinfo Review Mining MCP

[Pangolinfo Review Mining MCP server]

A differentiated, review-mining-only MCP server for Amazon product reviews. It mines what buyers complain about, how sentiment trends, and which complaint themes dominate — it does not duplicate the general Pangolinfo Amazon Data MCP.

How it differs from the general Amazon Data MCP

General Amazon Data MCPThis Review Mining MCP
Scope19+ tools: product search, product detail, best sellers, new releases, sellers, categories, niches, WIPO, SERP, Alexa5 tools, reviews only
IntentGeneral Amazon commerce intelligenceReview mining: complaints, sentiment, complaint topics
AnalysisReturns raw dataLocal analysis on top of fetched reviews (keyword-theme tagging, distribution, monthly trend, combined report)
UpstreamHosted Pangolinfo MCP serverSame hosted server's get_amazon_reviews tool, as an MCP client

If you need product search or anything beyond reviews, use the general MCP. If you need to know what buyers complain about and how that changes over time, use this one.

Architecture

MCP client (Claude Code, Cursor, Codex, ...)  └─ stdio ─► this server (src/index.ts)        └─ Streamable HTTP MCP client ─► https://mcp.pangolinfo.com/mcp              └─ upstream tool: get_amazon_reviews        └─ local analysis (src/analysis.ts): tagging, sentiment, topics

The server is a bridge plus analysis. It stores no API key and no review data; the key is sent only as an Authorization: Bearer header to the hosted server, taken from the environment.

Why a bridge instead of direct REST calls: the Pangolinfo docs page for the Review API lists parameters and response fields, but its request-URL and example blocks did not survive text extraction, so the direct REST endpoint is unconfirmed and deliberately not hardcoded here. The bridge path uses only confirmed facts: the hosted endpoint (https://mcp.pangolinfo.com/mcp, from the official amazon-data-mcp README) and the upstream get_amazon_reviews tool name + input schema (confirmed live via tools/list on 2026-10-26, a free metadata call). See TODOs below for the direct-REST follow-up.

Setup

Requirements: Node.js 18 or newer (developed/tested on Node 24).

bash
npm installnpm run build

Run (live mode):

bash
export PANGOLINFO_API_KEY="your-key-here"   # never commit a real keynode dist/src/index.js

Without a key the server still starts and answers initialize and tools/list; live tool calls fail with a clear message until PANGOLINFO_API_KEY is set.

Run (mock mode, no network, no key — for tests and demos):

bash
PANGOLINFO_REVIEW_MINING_MOCK=1 node dist/src/index.js

MCP client configuration (example, Claude Code style):

json
{  "mcpServers": {    "pangolinfo-review-mining": {      "command": "node",      "args": ["/absolute/path/to/pangolinfo-review-mining-mcp/dist/src/index.js"],      "env": { "PANGOLINFO_API_KEY": "your-key-here" }    }  }}

Environment variables:

VariableRequiredPurpose
PANGOLINFO_API_KEYYes (live mode)Bearer token for the hosted Pangolinfo MCP server
PANGOLINFO_MCP_URLNoUpstream MCP endpoint; default https://mcp.pangolinfo.com/mcp
PANGOLINFO_REVIEW_MINING_MOCKNo=1 enables mock mode (mocked reviews, no network)

Tools (5, all review-mining only)

ToolWhat it doesKey inputs
get_product_reviewsPaginated review fetch for one ASIN, with billing (pointCost) reported verbatimasin (required), site, pageCount (1–10), filterByStar, sortBy, mediaType
mine_complaintsComplaint signals from negative (critical, 1–3 star) reviews, each tagged with themes + firing keyword; optional minHelpful filterasin, site, pageCount, minHelpful
review_sentiment_summaryRating distribution, average star, negative/critical/positive shares, monthly trendasin, site, pageCount
top_complaint_topicsRanked complaint themes with counts, shares, example titlesasin, site, pageCount, topN
review_mining_reportCombined per-ASIN summary: totals, sentiment, top topics, most-endorsed complaints, billing; cites the Oct 12 baseline for B0FQFB8FMG as historical reference onlyasin, site, pageCount

Complaint themes (English keyword heuristic, v1 — transparent, not a classifier): quality_defect, battery_power, connectivity, comfort_fit, sound_quality, shipping_delivery, returns_refunds, value_price, authenticity, app_software. Every match reports the keyword that fired.

Marketplaces (site): amz_us (default), amz_de, amz_uk, amz_jp, amz_fr, amz_it, amz_es, amz_br, amz_au, amz_mx, amz_in, amz_eg, amz_ae, amz_ca.

Billing

  • The Review API is documented at 5 credits/page (docs "积点消耗: 5个/次" and per-page billing in bizContext.pageCount), and a measured Oct 2026 get_amazon_reviews call returned pointCost: 5 for one page.
  • Discrepancy flagged: the upstream MCP tool description currently states 10 credits/page. This server does not resolve that discrepancy by assertion — every tool reports pointCost exactly as returned by the upstream response, and the billing note in each response names both figures.
  • Free tier: 60 credits/month, recurring. At the measured 5/page rate that covers ~12 review pages/month; budget pageCount accordingly. review_mining_report makes two upstream fetches (all_stars + critical), so it costs twice one fetch.
  • The stored Pangolinfo API key expired (measured 2026-10-24). Live end-to-end testing is pending on the key being reconnected; see TEST_REPORT.md.

Tests

bash
npm test   # builds, then runs node --test on dist/test/

17 tests, all passing at build time (see TEST_REPORT.md): analysis unit tests, tool-handler tests against the mocked upstream, the Oct 12 baseline fixture test, and a stdio smoke test that spawns the real server in mock mode and speaks MCP JSON-RPC to it. All test review data is mocked (synthetic authors "Mock Reviewer A" …); the only real measured numbers in the repo are the aggregate baseline in fixtures/airpods-pro-3-baseline.json (4,268 total / 1,392 negative reviews for ASIN B0FQFB8FMG, measured 2026-10-12, reference only).

Limitations (read before presenting results as fact)

  • Complaint tagging is an English keyword heuristic, not sentiment ML. Non-English reviews will under-match until lexicons for their languages are added.
  • Star values come from strings like "5.0 out of 5 stars"; unparseable stars fall into unknown and are excluded from averages.
  • Dates come from strings like "October 14, 2026"; unparseable dates fall into the unknown monthly bucket.
  • totalReviews is returned by upstream as a string and may be empty when unavailable; it is passed through, not computed.
  • review_mining_report fetches all_stars + critical, so its review counts are per-fetch, not a single de-duplicated sample.

Publication status (2026-10-26)

mcprush accepts MCP servers or agent skills only (verified 2026-10-26: its server wizard requires a repo / npm / PyPI / container / hosted MCP endpoint and passes initialize + tools/list verification).

  • GitHub repo: live at https://github.com/Pangolin-spg/pangolinfo-review-mining-mcp
  • npm package: pangolinfo-review-mining-mcp 0.1.1, published 2026-10-26 (0.1.0 was the first publish the same day; 0.1.1 adds the mcpName field required by the MCP Registry)
  • mcprush listing: submitted 2026-10-26, in review (expected URL: https://mcprush.com/pangolin-spg/pangolinfo-review-mining-mcp)
  • Server-card info needed: name, one-line description, and a paragraph of at least 120 characters on what it does and who it is for.
  • Pricing: free plan (or priced plans with name, monthly price, call allowance); review takes up to two working days.
  • No video/intro URL field was visible in the submission form's expanded steps during the read-only check; if one appears, the project's intro videos are the Pangolinfo EN/ZH videos on file.
  • Backlink rule (standing): any link back to pangolinfo.com content in a listing must carry ?utm_source=<platform>&utm_medium=referral (plus utm_content=<product> when one platform carries several products) for GA4 referral attribution.

TODOs / unconfirmed items

  1. Direct REST endpoint (unconfirmed): the docs page's request-URL and example blocks did not extract, and no workspace file records the Review API's REST URL. The bridge design avoids needing it, but a direct-REST mode should not be added until the URL is confirmed from docs or a measured call.
  2. Live end-to-end test (done 2026-10-26): verified with a temporary 7-day key: get_product_reviews + mine_complaints on ASIN B0FQFB8FMG, 5 credits/page each, 4,446 total reviews / 1,457 critical at time of test.
  3. Billing discrepancy (resolved 2026-10-26): live pointCost = 5/page; the upstream tool description's 10/page is stale.
  4. Publish (done 2026-10-26): GitHub repo live, npm 0.1.0 published, mcprush listing submitted (in review).

来源:README.md,提交 6fc581d

工具

5

get_product_reviews

Fetch paginated Amazon product reviews for one ASIN via Pangolinfo, with the upstream billing (pointCost) reported verbatim. Billing: docs and measured calls show 5 credits/page; the upstream tool description currently states 10 credits/page — this tool reports pointCost as returned, it does not assert a rate.

输入参数asin必填sitesortBymediaTypepageCountfilterByStar

mine_complaints

Extract complaint signals from negative (critical, 1-3 star) reviews for one ASIN. Tags each complaint review with human-readable themes (quality_defect, battery_power, connectivity, comfort_fit, sound_quality, shipping_delivery, returns_refunds, value_price, authenticity, app_software) using a transparent English keyword heuristic — not a classifier. Optionally filter to complaints with at least minHelpful helpful votes.

输入参数asin必填sitepageCountminHelpful

review_sentiment_summary

Rating distribution, average star, negative/critical/positive shares, and a monthly sentiment trend for one ASIN's reviews. Pure local computation over fetched reviews.

输入参数asin必填sitepageCount

top_complaint_topics

Ranked complaint themes across negative reviews for one ASIN, with counts, shares, and example review titles for traceability.

输入参数asin必填sitetopNpageCount

review_mining_report

Combined per-ASIN review-mining summary: totals, rating distribution, monthly trend, top complaint topics, most-endorsed complaints, and billing as returned. When the ASIN matches the documented Oct 12, 2026 baseline (B0FQFB8FMG), the report also cites that historical baseline for reference, clearly labeled as historical — never as current data.

输入参数asin必填sitepageCount

版本历史

2
  1. v0.1.2最新Oct 31, 2026
  2. v0.1.1Oct 26, 2026

评分

已满足适用检查项的 90%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通25 分
  • 已声明认证方式10 分

    请在注册中心条目中声明凭据请求头,或保持端点能够响应探测,以便判定其认证方式。

  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分

其他 Review 集成