Extract shoppable product mentions from newsletters with affiliate signals. Free tier 200/day.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
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.
β If this saves you time, please star the repo β it helps other developers find it.
Live endpoint:
https://newsletter-commerce-mcp.sincetoday.workers.dev/mcpΒ· 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) |
extract_newsletter_productsReturns:
analyze_newsletter_sponsorsReturns CPM estimate, read-through rate, and sponsor-reader fit score per sponsor section.
track_product_trendsRequires 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_sectionFormats 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):
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 | β | OpenAI API key |
AGENT_ID | No | anonymous | Agent identifier for rate limiting |
MCP_API_KEYS | No | β | Comma-separated paid API keys |
CACHE_DIR | No | ./data/cache.db | SQLite cache path |
PAYMENT_ENABLED | No | false | Set true to enforce limits |
MIT β Since Today Studio
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