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Extract shoppable products from newsletter content with affiliate signals.
Extract shoppable products from newsletter content with affiliate signals.
Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. Trust signals: trusted author (4/4 approved). 1 finding(s) downgraded by scanner intelligence.
4 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: OPENAI_API_KEY
Environment variable: MCP_API_KEYS
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-teamsincetoday-newsletter-commerce-mcp": {
"env": {
"MCP_API_KEYS": "your-mcp-api-keys-here",
"OPENAI_API_KEY": "your-openai-api-key-here"
},
"args": [
"-y",
"newsletter-commerce-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Turn newsletters into affiliate revenue. Extract sponsored products, brand mentions, and affiliate signals from any Substack, Ghost, or Beehiiv issue. Then auto-generate a shoppable "Products in this edition" section ready to paste into your newsletter. F1=100% on eval suite. Free tier: 200 calls/day.
⭐ If this saves you time, please star the repo — it helps other developers find it.
Live endpoint:
https://newsletter-commerce-mcp.sincetoday.workers.dev/mcp· See examples
Extract product mentions, score sponsors, and track affiliate trends from newsletters. Supports Substack, Ghost, Beehiiv, and plain text. Built on x402, the open payment standard backed by Shopify, Google, Microsoft, Visa, and the Linux Foundation.
| Tool | Description |
|---|---|
extract_newsletter_products | Extract products, affiliate links, and sponsor mentions from a newsletter issue |
analyze_newsletter_sponsors | Score sponsor sections by CPM, read-through rate, and audience fit |
track_product_trends | Compare product mentions across multiple newsletter issues to surface trending products and brand patterns |
generate_newsletter_products_section | Format extracted products into a 'Products in This Edition' footer section (markdown or HTML) |
# Install
npm install newsletter-commerce-mcp
# Configure
cp .env.example .env
# Edit .env: set OPENAI_API_KEY
# Run (stdio MCP server)
npx newsletter-commerce-mcp
{
"mcpServers": {
"newsletter-commerce": {
"command": "npx",
"args": ["newsletter-commerce-mcp"],
"env": {
"OPENAI_API_KEY": "sk-..."
}
}
}
}
extract_newsletter_products{
"content": "Newsletter HTML or plain text (max 200k chars)",
"newsletter_id": "optional-cache-key",
"format": "html",
"api_key": "optional-paid-key"
}
Returns:
{
"newsletter_id": "swipe-file-issue-47",
"products": [
{
"name": "Notion AI",
"category": "saas",
"mention_context": "running my entire writing workflow through Notion AI",
"recommendation_strength": "strong",
"affiliate_link": null,
"confidence": 0.94,
"is_sponsored": false
}
],
"sponsor_sections": [...],
"_meta": { "processing_time_ms": 1620, "ai_cost_usd": 0.0028, "cache_hit": false }
}
analyze_newsletter_sponsors{
"content": "Newsletter HTML or plain text",
"newsletter_id": "optional",
"api_key": "optional"
}
Returns CPM estimate, read-through rate, and sponsor-reader fit score per sponsor section.
track_product_trends{
"newsletter_ids": ["issue-45", "issue-46", "issue-47"],
"category_filter": ["saas", "books"]
}
Requires prior extract_newsletter_products calls for each newsletter_id. Returns trend data including top_category, avg_recommendation_strength, and brand per product trend.
generate_newsletter_products_section{
"newsletter_id": "swipe-file-issue-47",
"format": "markdown",
"style": "full",
"api_key": "optional"
}
Formats extracted products into a ready-to-paste 'Products in This Edition' section. Pass newsletter_id (uses cached extraction) or products[] directly. format: markdown (default) or html. style: full (default, grouped by endorsement strength with context quotes) or minimal (compact list).
Real extraction from a TLDR Tech newsletter (live eval: F1=88%, 95/100 score, $0.00051/call, 7390ms):
{
"newsletter_id": "tldr-2024-03-07",
"products": [
{
"name": "Groq",
"category": "saas",
"mention_context": "Groq has launched public API access — runs Llama 2 at 300 tokens/second",
"confidence": 0.94,
"recommendation_strength": "neutral"
},
{
"name": "Devin (Cognition AI)",
"category": "saas",
"mention_context": "first AI software engineer — benchmarks show it can complete real GitHub issues end-to-end",
"confidence": 0.91,
"recommendation_strength": "strong"
}
]
}
See /examples endpoint for full output with value narrative: https://newsletter-commerce-mcp.sincetoday.workers.dev/examples
MCP_API_KEYS with valid keys| Variable | Required | Default | Description |
|---|---|---|---|
OPENAI_API_KEY | Yes | — | OpenAI API key |
AGENT_ID | No | anonymous | Agent identifier for rate limiting |
MCP_API_KEYS | No | — | Comma-separated paid API keys |
CACHE_DIR | No | ./data/cache.db | SQLite cache path |
PAYMENT_ENABLED | No | false | Set true to enforce limits |
npm install
npm run typecheck # Zero type errors
npm test # All tests pass
npm run build # Compile to dist/
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