Which integrations are available for this server?

Fetches and analyzes Amazon product reviews via ASIN across 10 markets (US, CA, MX, GB, DE, FR, IT, ES, JP, AU) using the Shulex VOC OpenAPI data layer, providing sentiment analysis, pain points, and listing improvements. Accepts eBay review CSV/Excel files for analysis, using fuzzy column detection to handle varying formats and generating the same VOC report and dashboard. Accepts Helium 10 CSV/Excel exports for analysis, using fuzzy column detection to handle varying formats and generating the same VOC report and dashboard. Accepts Shopify review CSV/Excel files for analysis, using fuzzy column detection to handle varying formats and generating the same VOC report and dashboard.

voc-amazon-reviews by mguozhen

TL;DR

Two inputs, six tools, three outputs.

   ┌─────────────┐                                            ┌──────────────┐
   │   ASIN      │──┐                                       ┌─│ Markdown     │
   └─────────────┘  │      ┌─────────────────────────┐      │ │ report       │
                    ├──────▶ 6 agent-callable tools  ├──────┤ ├──────────────┤
   ┌─────────────┐  │      └─────────────────────────┘      │ │ Structured   │
   │  CSV / XLSX │──┘   fetch_reviews   analyze_csv         │ │ JSON         │
   └─────────────┘      analyze_reviews voc_full            │ ├──────────────┤
                        extract_listing_improvements        └─│ Black-gold   │
                        render_dashboard                      │ HTML deck    │
                                                              └──────────────┘

Inputs — Amazon ASIN (auto-fetched via Shulex VOC OpenAPI, 10 markets) or any review CSV / Excel (Helium 10 / eBay / Shopify / custom — fuzzy column detection)
Outputs — Markdown report · structured JSON · standalone HTML dashboard
Surface — MCP server (works in Claude Code / Cursor / Cline / Continue) and Skill (works in Claude Code)

Quick start

Option A — As an MCP server (recommended)

# 1. Get a free Shulex VOC API key (starter credits free)
#    https://apps.voc.ai/openapi
export VOC_API_KEY="your-key"
export ANTHROPIC_API_KEY="sk-ant-..."   # only needed for extract_listing_improvements

# 2. Install
git clone https://github.com/mguozhen/voc-amazon-reviews
cd voc-amazon-reviews
pip install -r mcp_server/requirements.txt

# 3. Register with your agent (one line)
claude mcp add review-analyzer -- python -m mcp_server.server

Now ask any Claude Code / Cursor / Cline session:

Run a VOC report on B08N5WRWNW, render the dashboard, and write it to ~/Desktop/voc.html.

The agent will call voc_full → render_dashboard and hand you the file.

Option B — One-shot CLI

bash voc.sh B08N5WRWNW --limit 100 --market US

Option C — Bring your own reviews (CSV)

# Drop in any reviews CSV (Helium 10 export, eBay scrape, Shopify, custom)
python -c "from mcp_server.tools import analyze_csv, render_dashboard; \
  r = analyze_csv('reviews.csv', product_name='My Product'); \
  render_dashboard(r, output_path='dashboard.html')"

Tools

#	Tool	Input	Use when
1	`fetch_reviews`	ASIN	You want raw reviews; you'll analyze them yourself
2	`analyze_reviews`	reviews JSON	You already have reviews and want the VOC report
3	`voc_full`	ASIN	Default "give me a VOC report" — fetch + analyze in one call
4	`extract_listing_improvements`	ASIN	★ Differentiator — copy-ready title / 5 bullets / description grounded in customer language
5	`analyze_csv`	CSV / Excel path or URL	The product is NOT on Amazon, or you have your own scrape
6	`render_dashboard`	VOC report	Generate a standalone black-gold HTML dashboard, no external deps

All 6 tools speak MCP. All return JSON-serializable dicts. Full schemas in mcp_server/README.md.

Data layer — why this is the moat

Most "AI review tools" are a thin LLM wrapper over a brittle scraper. We invert that. The data layer is the moat:

	Typical seller-tool data layer	review-analyzer
Source	Web scraper / undocumented scrape API	Paid Shulex VOC OpenAPI
Reliability	Breaks when Amazon updates HTML	API-grade, no DOM dependencies
Markets	US-only or 2-3 markets	10: US, CA, MX, GB, DE, FR, IT, ES, JP, AU
Volume	10–50 reviews (free-tier cap)	Up to 1,000 reviews per ASIN
Freshness	Daily snapshots, sometimes cached for days	Live pull
Schema	Strings only	Full: verified-purchase, helpful votes, vine, variant, dates
Non-English markets	Often broken / omitted	Native captures + AI translation
Access	Locked behind a UI	curl + JSON, fully scriptable, MCP-ready

For non-Amazon platforms, analyze_csv accepts any review file — fuzzy column matching detects 内容 / 评价 / body / review / content so you don't have to reformat. Bring data from anywhere, get the same VOC report.

vs. the alternatives

	review-analyzer	Helium 10 / Data Dive	review-analyzer-skill (Buluu)	Generic review scrapers
Input	ASIN or CSV	ASIN (manual UI)	CSV only	URL
Markets	10	1-3	depends on user's data	1
Output	JSON + Markdown + HTML dashboard	UI dashboard (locked)	CSV + MD + HTML dashboard	Raw CSV
MCP-callable	✅	❌	❌ Claude Code only	❌
Listing copy gen	✅ `extract_listing_improvements` (cite-by-pain-point)	Keyword research only	❌	❌
Cost	Shulex API + Anthropic API ($0.05-0.20/listing)	$99-249/month subscription	Free (uses your Claude quota)	Free, brittle
Open source	✅ MIT	❌	✅ MIT	varies

Credit & inspiration: The 22-dimension tag system, fuzzy CSV column detection, and black-gold dashboard aesthetic were inspired by buluslan/review-analyzer-skill (MIT). We adapted them onto an MCP-native architecture with the Shulex VOC OpenAPI data layer.

Architecture

mcp_server/
├── server.py                  # 6 @mcp.tool decorators
├── tools.py                   # implementations (subprocess wrappers + Anthropic SDK)
├── csv_loader.py              # fuzzy column detection for CSV/Excel input
├── dashboard.py               # HTML rendering
├── dashboard_template.html    # black-gold template (placeholders)
├── tag_system.yaml            # 22-dim tag schema (customizable per category)
├── schemas.py                 # pydantic structured-output models
└── tests/                     # 36 unit tests (subprocess + Anthropic mocked)

fetch.sh / analyze.sh / voc.sh   # shell pipeline behind tools 1-3

fetch + analyze loop: shell scripts (proven, reproducible, easy to debug)
listing rewrites: Anthropic SDK direct (claude-opus-4-7 + adaptive thinking + prompt caching on the system rubric)
dashboard: pure stdlib HTML rendering, no node / no react

Distribution / where to find us

Channel	Status
punkpeye/awesome-mcp-servers PR #6528	✅ Open
cline/mcp-marketplace issue #1602	✅ Open
Glama	🟢 Auto-indexed via GitHub topics
mcp.directory	🟢 Auto-pull
mcp.so / PulseMCP	🟡 Pending (manual form submit)
Official MCP Registry	🟡 Pending PyPI publish (W2)

Roadmap

Drop in CSV / Excel (any platform, fuzzy column detect)
22-dimension tag system (YAML-configurable)
Black-gold HTML dashboard tool
6 MCP tools shipped
npx skills add mguozhen/review-analyzer one-line install
CLI subprocess engine option (use your Claude subscription, $0 API)
PyPI publish + official MCP Registry submission
Smithery / mcp.so / PulseMCP form submissions

License

MIT. See LICENSE.

Acknowledgments: Tag schema, CSV column detection, and dashboard visual design inspired by buluslan/review-analyzer-skill. Data layer powered by Shulex VOC OpenAPI.

voc-amazon-reviews

TL;DR

Quick start

Option A — As an MCP server (recommended)

Option B — One-shot CLI

Option C — Bring your own reviews (CSV)

Tools

Data layer — why this is the moat

vs. the alternatives

Architecture

Distribution / where to find us

Roadmap

License

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