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

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)

Quick Start

bash

# 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

config.json

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

Tool Reference

`extract_newsletter_products`

config.json

{
  "content": "Newsletter HTML or plain text (max 200k chars)",
  "newsletter_id": "optional-cache-key",
  "format": "html",
  "api_key": "optional-paid-key"
}

Returns:

config.json

{
  "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`

config.json

{
  "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`

config.json

{
  "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`

config.json

{
  "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: F1=88%, 95/100 score, $0.00051/call, 7390ms):

config.json

{
  "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

Pricing

Free tier: 200 calls/day per agent (no API key required)
Paid: $0.01/call — set MCP_API_KEYS with valid keys

Environment Variables

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

Development

Terminal

npm install
npm run typecheck   # Zero type errors
npm test            # All tests pass
npm run build       # Compile to dist/

License

MIT — Since Today Studio