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Podcast Commerce MCP

Extracts products, sponsors, and trends from podcast transcripts, then formats product data for shoppable show notes.

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Install Podcast Commerce MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "podcast-commerce-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "podcast-commerce-mcp"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Automated check passed— started and listed 4 tools correctly (Today).

Overview

podcast-commerce-mcp MCP server analyzes podcast transcripts to identify products, brands, sponsor segments, and recommendation strength. It provides MCP tools for single-episode extraction, sponsor analysis, cached trend tracking, cross-show comparisons, and show notes generation. The package runs locally over stdio with an OpenAI API key, or agents can connect to a hosted MCP endpoint without installing it. Reach for it when podcast research, sponsorship analysis, or commerce-focused episode publishing requires structured product data.

Use cases

  • Extract products from episode transcripts
  • Analyze sponsor reads and estimated delivery
  • Compare product mentions across episodes
  • Generate shoppable show notes

Key features

  • Transcript product and brand extraction
  • Confidence and recommendation-strength scoring
  • Sponsor segment and read-through analysis
  • Cross-episode and cross-show product comparisons
  • Formatted shoppable show notes

Tools4

What Podcast Commerce MCP lets your agent do. Expand a tool for its parameters.

Verified live~1.4k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

7/7 checks healthy over the last 51d

What it can do

From the 4 tools this server returned when we ran it.

Makes network requests · 1Read-only tools · 4
Network at startup3 connections
Files written at startupNone
Install scriptsNone
npm provenanceNot published
Package age237 days
Maintainers1

Observed 2026-10-29 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 14d ago via OSV.dev · podcast-commerce-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads100Package downloads in the last 30 days.
Last commit6mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed4Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Podcast Commerce MCP works

What podcast-commerce-mcp MCP server does

The podcast-commerce-mcp MCP server turns podcast transcript text into structured commerce data for AI agents. Its extraction workflow identifies mentioned products and brands, records surrounding context and speaker information, and assigns confidence and recommendation-strength values. Results may also include sponsor segments and affiliate-related fields.

The server supports both individual episode analysis and comparisons across a catalog of previously processed episodes. It can identify sponsor reads, estimate how much of a sponsor segment was delivered, follow product mention patterns over time, and rank products across multiple shows after resolving entities that refer to the same brand.

How it works

A client sends a transcript, or a URL to a text file, to the extraction tool. Optional inputs include an episode identifier for caching and category filters such as SaaS or physical goods. The returned data includes products, mention counts, context, speakers, confidence values, recommendation strength, sponsor information, and processing metadata.

Trend analysis uses episode identifiers that have already been extracted and cached. It reports whether product mentions are rising, stable, or falling, along with average recommendation strength and the leading category. Cross-show analysis accepts show identifiers and can filter by minimum show count or confidence. Its results include show coverage, average confidence, and whether recommendations are unanimous, majority-based, mixed, or rare.

Setup and configuration

The podcast-commerce-mcp MCP server can be installed from npm with npm install podcast-commerce-mcp and started locally with npx podcast-commerce-mcp. Local execution requires OPENAI_API_KEY. Optional settings identify an agent, configure paid API keys, select the SQLite cache location, and enable payment enforcement.

Agents can also use the hosted streamable MCP endpoint at https://podcast-commerce-mcp.sincetoday.workers.dev/mcp. The documented remote configuration does not require an API key and provides a free allowance of 200 calls per day per agent. Paid usage is listed as $0.01 per call and uses configured MCP API keys.

Tools and capabilities

  • extract_podcast_products finds products and brands in a transcript and returns confidence, context, speaker, recommendation, and related metadata.
  • analyze_episode_sponsors identifies sponsor segments and estimates read-through rates.
  • track_product_trends compares products across cached episodes.
  • compare_products_across_shows ranks products mentioned by multiple shows with entity resolution.
  • generate_show_notes_section converts previously extracted product data into a formatted, shoppable show notes block. Markdown and a full style are documented options.

Limitations and notes

Trend tracking and show notes generation depend on data that was previously extracted and cached. The local package depends on an OpenAI API key, while the hosted endpoint has its own daily free-use limit.

Extraction quality and confidence values describe the server's analysis; they should be reviewed before publishing affiliate links, sponsor claims, or commercial recommendations. The repository lists MIT licensing and provides development commands for type checking, tests, and builds.

Read the full README View source on GitHub

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Frequently Asked Questions about Podcast Commerce MCP

podcast-commerce-mcp is an MCP server that connects AI agents with podcast transcript analysis. Its main tools extract products, analyze episode sponsors, track product trends across cached episodes, compare products across shows, and generate formatted shoppable show notes sections from extracted product data.

Install it with `npm install podcast-commerce-mcp`, set `OPENAI_API_KEY`, and run `npx podcast-commerce-mcp`. You can also connect to the hosted MCP endpoint without a local install; the README provides configuration examples for Claude Desktop and Claude Code.

It exposes a standard MCP endpoint and a local stdio process, so connection details would depend on Cursor's MCP configuration support; the repository only provides explicit examples for Claude Desktop and Claude Code.

It compares products across episode IDs that were previously extracted and cached. The results include each product's trend direction, average recommendation strength, and top category; optional category filters can narrow the comparison.

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