Newsletter Commerce Intelligence MCP

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.

Tools

Quick Start

# 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

MCP Client Config

{
  "mcpServers": {
    "newsletter-commerce": {
      "command": "npx",
      "args": ["newsletter-commerce-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Tool Reference

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).

Example Output

Real extraction from a TLDR Tech newsletter (live eval

... View full README on GitHub