Pangolinfo Review Mining MCP

io.github.Pangolin-spgPublished by GitHub account Pangolin-spgv0.1.2Updated Nov 3, 2026

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

The publisher's own server: Review Expert

VerifiedSTDIODesktop onlyData & AnalyticsBusiness & Commerce

Overview

AI-generated overview

Mines Amazon product reviews for one ASIN: complaint themes, sentiment trends and a combined review-mining report, via the Pangolinfo API.

What you can do
  • Fetch paginated Amazon reviews for an ASIN, with billing reported as returned
  • Extract complaint signals from critical 1-3 star reviews, tagged with themes and the keyword that fired
  • Summarize rating distribution, average star, positive/negative shares and a monthly sentiment trend
  • Rank complaint topics with counts, shares and example review titles
  • Produce a combined per-ASIN report with totals, trend, top topics and most-endorsed complaints
When to use it
Use it when you need to know what buyers complain about in an Amazon product's reviews and how that changes over time, rather than general product search or catalog data. It is a review-only companion to the broader Pangolinfo Amazon Data server.
Requirements
Node.js 18 or newer, run as a local stdio process. A Pangolinfo API key in PANGOLINFO_API_KEY is required for live calls; without it the server starts but tool calls fail. Network access to the hosted Pangolinfo MCP endpoint, which can be overridden with PANGOLINFO_MCP_URL. A mock mode is available via PANGOLINFO_REVIEW_MINING_MOCK.
Before you install
Live calls consume Pangolinfo credits (documented at 5 per page, with an upstream description stating 10; the free tier is 60 credits/month), and the combined report makes two fetches so it costs double. The API key is sent as a bearer token to the hosted Pangolinfo server. Complaint tagging is an English keyword heuristic, so non-English reviews under-match; treat results as signals, not verified fact.

Installation

In SourceWeft

  1. Open Pangolinfo Review Mining MCP in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

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).

Source: README.md at commit 6fc581d

Tools

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.

ParametersasinrequiredsitesortBymediaTypepageCountfilterByStar

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.

ParametersasinrequiredsitepageCountminHelpful

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.

ParametersasinrequiredsitepageCount

top_complaint_topics

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

ParametersasinrequiredsitetopNpageCount

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.

ParametersasinrequiredsitepageCount

Version history

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

Score

90% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts
  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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