PluginWorld
Pa

Pangolinfo Review Mining MCP

MCP

Enables mining Amazon product reviews for one ASIN per request — fetching paginated reviews, surfacing complaint signals from critical reviews, summarizing sentiment and rating trends over time, ranking complaint themes, and producing a combined per-ASIN report across 14 marketplaces.

@Pangolin-spg · v0.1.1 · MIT License · updated today

SECURITY

B

SCORE

68

STARS

▲ 0

PLUG IN

git clone https://github.com/Pangolin-spg/pangolinfo-review-mining-mcp.git

See the README to configure this MCP server

README

Pangolinfo Review Mining MCP

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 MCP This Review Mining MCP
Scope 19+ tools: product search, product detail, best sellers, new releases, sellers, categories, niches, WIPO, SERP, Alexa 5 tools, reviews only
Intent General Amazon commerce intelligence Review mining: complaints, sentiment, complaint topics
Analysis Returns raw data Local analysis on top of fetched reviews (keyword-theme tagging, distribution, monthly trend, combined report)
Upstream Hosted Pangolinfo MCP server Same 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).

npm install
npm run build

Run (live mode):

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

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

PANGOLINFO_REVIEW_MINING_MOCK=1 node dist/src/index.js

MCP client configuration (example, Claude Code style):

{
  "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:

Variable Required Purpose
PANGOLINFO_API_KEY Yes (live mode) Bearer token for the hosted Pangolinfo MCP server
PANGOLINFO_MCP_URL No Upstream MCP endpoint; default https://mcp.pangolinfo.com/mcp
PANGOLINFO_REVIEW_MINING_MOCK No =1 enables mock mode (mocked reviews, no network)

Tools (5, all review-mining only)

Tool What it does Key inputs
get_product_reviews Paginated review fetch for one ASIN, with billing (pointCost) reported verbatim asin (required), site, pageCount (1–10), filterByStar, sortBy, mediaType
mine_complaints Complaint signals from negative (critical, 1–3 star) reviews, each tagged with themes + firing keyword; optional minHelpful filter asin, site, pageCount, minHelpful
review_sentiment_summary Rating distribution, average star, negative/critical/positive shares, monthly trend asin, site, pageCount
top_complaint_topics Ranked complaint themes with counts, shares, example titles asin, site, pageCount, topN
review_mining_report Combined per-ASIN summary: totals, sentiment, top topics, most-endorsed complaints, billing; cites the Oct 12 baseline for B0FQFB8FMG as historical reference only asin, 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

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).

SIMILAR PLUGINS