Podcast Commerce Intelligence MCP

Turn podcast transcripts into affiliate revenue. Give any episode transcript to an AI agent — get back every product mentioned, who said it, how strongly they recommended it, and which affiliate network carries it. 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://podcast-commerce-mcp.sincetoday.workers.dev/mcp · See examples

Extract product mentions, sponsor segments, and product trends from podcast transcripts. Built on x402, the open payment standard backed by Shopify, Google, Microsoft, Visa, and the Linux Foundation.

Tools

Quick Start

# Install
npm install podcast-commerce-mcp

# Configure
cp .env.example .env
# Edit .env: set OPENAI_API_KEY

# Run (stdio MCP server)
npx podcast-commerce-mcp

Connect in Claude Code — No Install Required

Add to your claude_desktop_config.json or use /add-mcp in Claude Code. Free tier: 200 calls/day, no API key needed:

{
  "mcpServers": {
    "podcast-commerce": {
      "url": "https://podcast-commerce-mcp.sincetoday.workers.dev/mcp"
    }
  }
}

MCP Client Config (local/stdio)

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

Tool Reference

extract_podcast_products

{
  "transcript": "Raw text or URL to a .txt file",
  "episode_id": "optional-cache-key",
  "category_filter": ["saas", "physical_goods"],
  "api_key": "optional-paid-key"
}

Returns:

{
  "episode_id": "...",
  "products": [
    {
      "name": "Notion",
      "category": "saas",
      "mention_context": "I use Notion every day...",
      "speaker": "Host",
      "confidence": 0.9,
      "recommendation_strength": "strong",
      "affiliate_link": null,
      "mention_count": 2
    }
  ],
  "sponsor_segments": [...],
  "_meta": { "processing_time_ms": 1200, "ai_cost_usd": 0.001, "cache_hit": false }
}

analyze_episode_sponsors

{
  "transcript": "...",
  "episode_id": "optional",
  "api_key": "optional"
}

track_product_trends

{
  "episode_ids": ["ep1", "ep2", "ep3"],
  "category_filter": ["saas"]
}

Requires episodes to be previously extracted and cached. Returns trends[] with brand, trend (rising/stable/falling), avg_recommendation_strength, and top_category.

compare_products_across_shows

{
  "show_ids": ["show-a", "show-b"],
  "min_show_count": 2,
  "min_confidence": 0.85
}

Ranks products by how many shows mention them. Returns products[] with brand, show_count, avg_confidence, recommendation_consensus (unanimous/majority/mixed/rare).

generate_show_notes_section

{
  "episode_id": "previously-extracted-id",
  "format": "markdown",
  "style": "full"
}

Formats cached product data as a shoppable show notes block. Returns a formatted string rea

... View full README on GitHub