Skip to content
Agentmart
Submit a tool

Podcast Commerce MCP

Extract product mentions from podcast transcripts with affiliate signals.

Connector by teamsincetoday · GitHub account verified by the MCP Registry

IndexedLicense unknownLocal (stdio)Remote (HTTP)
Write a review

Chalk Labs

Need help deploying Podcast Commerce MCP?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/teamsincetoday-podcast-commerce-mcp

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Extract product mentions from podcast transcripts with affiliate signals. Free tier 200/day.

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools
5 read · 0 write · 0 deleteRead-only
Secrets
2 (API keys, tokens or passwords)
Install scripts
Not scanned yet
Runs as
Node.js package

Tools by what they do 5

Read from the source code; the running server may list different tools.

Read 5 · Look things up without changing anything.

  • analyze_episode_sponsors
  • compare_products_across_shows
  • extract_podcast_products
  • generate_show_notes_section
  • track_product_trends

Secrets and settings it asks for

Secrets: values you paste in that let it act as you.

  • MCP_API_KEYS
  • OPENAI_API_KEY

Tools (5)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

  • analyze_episode_sponsors

    Identify and score podcast sponsor segments (host-read ads, mid-roll, pre-roll). Returns sponsor name, ad placement type, call-to-action URL, estimated read-through rate, and revenue metrics based on category benchmarks — not live ad-platform data. Use for advertising intelligence, CPM estimation, and sponsor outreach research. Use this tool when you need sponsor metrics only; for the full product and recommendation list use extract_podcast_products instead. Reuses cached extraction when episode_id matches a prior extract_podcast_products call. Example: episode_id='huberman-ep-301' → returns […

  • compare_products_across_shows

    Compare and rank product mentions across multiple podcast shows using cached extractions — no re-run or extra AI cost. Collapses a 3-call manual join into 1 tool call with entity resolution to match the same product across different show mentions. Returns ranked products with per-show context, average confidence, recommendation consensus, and brand aggregation. Use for multi-show affiliate research, 'best of' page generation, and cross-show brand ranking. Requires prior extract_podcast_products call for each show_id — returns error listing missing IDs if any are absent. Example: show_ids=['epi…

  • extract_podcast_products

    Extract affiliate products, sponsored brands, and host recommendations from a podcast transcript (text input only — does not process audio or video). Returns product name, category, confidence score, recommendation strength, and sponsor flag. Call this before analyze_episode_sponsors or track_product_trends — both tools reuse its cache. Use for podcast monetization, affiliate program discovery, and episode-to-episode trend tracking. Returns empty products array (not an error) when no product mentions are found. Example: episode_id='huberman-ep-301', category_filter=['physical_goods','supplemen…

  • generate_show_notes_section

    Format extracted podcast products into a shoppable show notes section. Returns a ready-to-paste markdown or HTML product list, grouped by endorsement strength (strong/moderate/mention), with affiliate links where resolved. Use after extract_podcast_products — either pass episode_id to use cached extraction, or pass products[] directly. Handles null affiliate_link gracefully (shows product name without link). Format: markdown (default) produces copy-paste text for show notes editors; html produces embeddable markup. Style: full (default) groups by endorsement with context quotes; minimal produc…

  • track_product_trends

    Compare affiliate product and brand mention frequency across multiple podcast episodes to detect rising, stable, and declining trends. Returns trend velocity, mention count per episode, and category breakdown. No AI call — computed from cached extraction data only. Use for affiliate marketing optimization, seasonal product tracking, and content calendar planning. Requires prior extract_podcast_products call for each episode_id — returns an error listing missing IDs if any are not in cache. Example: episode_ids=['ep-301','ep-302','ep-303'].

Signals

Stars
0
Forks
0
Open issues
0
Last commit
Apr 8, 2026 (6 months ago)
Last release
Apr 4, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Registry name
io.github.teamsincetoday/podcast-commerce-mcp
Version
0.1.3
Auth
API key
License
License unknown
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from teamsincetoday

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get Podcast Commerce MCP running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying Podcast Commerce MCP?

CallGet help
Cite this page

Podcast Commerce MCP. Agent Mart. https://agentmart.dev/connectors/teamsincetoday-podcast-commerce-mcp (last updated 2026-10-24)