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teamsincetoday

Newsletter Commerce Intelligence MCP

by teamsincetoday
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Newsletter Commerce Intelligence MCP

npm License: MIT Stars

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)

Related MCP server: Agentic Product Protocol MCP Server

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: 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

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

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

License

MIT — Since Today Studio

Install Server
A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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