Extract product mentions from podcast transcripts with affiliate signals. Free tier 200/day.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β we're steadily working through the catalog.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Podcast Commerce MCP.
extract_podcast_productsExtract products/brands from a transcript with confidence scores
analyze_episode_sponsorsIdentify sponsor segments and estimate read-through rates
track_product_trendsCompare product mentions across multiple episodes
compare_products_across_showsCross-show product ranking with entity resolution across multiple shows
generate_show_notes_sectionFormat extracted products as a shoppable show notes section
Turn podcast transcripts into affiliate revenue. Give any episode transcript to an AI agent β 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.
β If this saves you time, please star the repo β it helps other developers find it.
Live endpoint:
https://podcast-commerce-mcp.sincetoday.workers.dev/mcpΒ· 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.
| 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 |
Add to your claude_desktop_config.json or use /add-mcp in Claude Code. Free tier: 200 calls/day, no API key needed:
extract_podcast_productsReturns:
analyze_episode_sponsorstrack_product_trendsRequires episodes to be previously extracted and cached. Returns trends[] with brand, trend (rising/stable/falling), avg_recommendation_strength, and top_category.
compare_products_across_showsRanks products by how many shows mention them. Returns products[] with brand, show_count, avg_confidence, recommendation_consensus (unanimous/majority/mixed/rare).
generate_show_notes_sectionFormats cached product data as a shoppable show notes block. Returns a formatted string ready to paste into episode notes.
Real extraction from a Huberman Lab episode transcript (live eval: F1=89%, 96/100 score, $0.00046/call, 8100ms):
See /examples endpoint for full output with value narrative: https://podcast-commerce-mcp.sincetoday.workers.dev/examples
MCP_API_KEYS with valid keys| Variable | Required | Default | Description |
|---|---|---|---|
OPENAI_API_KEY | Yes | β | OpenAI API key |
AGENT_ID | No | anonymous | Agent identifier for rate limiting |
MCP_API_KEYS | No | β | Comma-separated paid API keys |
CACHE_DIR | No | ./data/cache.db | SQLite cache path |
PAYMENT_ENABLED | No | false | Set true to enforce limits |
MIT β Since Today Studio
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