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Extract product mentions from podcast transcripts with affiliate signals. Free tier 200/day.
About
Extract product mentions from podcast transcripts with affiliate signals. Free tier 200/day.
Security Report
Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. Trust signals: trusted author (5/5 approved). 1 finding(s) downgraded by scanner intelligence.
5 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: OPENAI_API_KEY
Environment variable: MCP_API_KEYS
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-teamsincetoday-podcast-commerce-mcp": {
"env": {
"MCP_API_KEYS": "your-mcp-api-keys-here",
"OPENAI_API_KEY": "your-openai-api-key-here"
},
"args": [
"-y",
"podcast-commerce-mcp-actor"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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
| Tool | Description |
|---|---|
extract_podcast_products | Extract products/brands from a transcript with confidence scores |
analyze_episode_sponsors | Identify sponsor segments and estimate read-through rates |
track_product_trends | Compare product mentions across multiple episodes |
compare_products_across_shows | Cross-show product ranking with entity resolution across multiple shows |
generate_show_notes_section | Format extracted products as a shoppable show notes section |
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 ready to paste into episode notes.
Example Output
Real extraction from a Huberman Lab episode transcript (live eval: F1=89%, 96/100 score, $0.00046/call, 8100ms):
{
"episode_id": "huberman-ep-312",
"products": [
{
"name": "AG1 (Athletic Greens)",
"brand": "AG1",
"category": "supplement",
"mention_context": "today's episode is brought to you by AG1. I've been taking it every morning for six months",
"confidence": 0.97,
"recommendation_strength": "strong"
},
{
"name": "Oura Ring",
"category": "physical_goods",
"mention_context": "I've been wearing it for sleep tracking for two years. They're not a sponsor, just a genuine rec",
"confidence": 0.95,
"recommendation_strength": "strong"
}
],
"sponsor_segments": [
{
"sponsor_name": "AG1",
"read_type": "host_read",
"estimated_read_through": 0.72,
"call_to_action": "code HUBERMAN for a free year's supply of Vitamin D"
}
]
}
See /examples endpoint for full output with value narrative: https://podcast-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_KEYSwith 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
Reviews
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