Podcast Recommendations — Podcast Discovery MCP 🎰🎙️

Podcast recommendations beyond the charts: long-tail podcast discovery, recommendations, and random spins — randomized probe queries surface shows the charts recycle forever, and every pick carries its feed URL, cadence, and typical episode length.

Ask any agent: "find me a podcast about Byzantine history I haven't heard of" or "surprise me with a podcast" — podcast-recommendations is the tool that answers. Zero API keys, zero configuration. Built on Apple's keyless iTunes podcast endpoints (verified) and direct RSS feed reads: a 300KB feed becomes ~200 tokens of show intelligence, all arithmetic done server-side.

Why this exists

Search APIs return the same chart toppers for every query; LLMs recommend the same famous podcasts everyone already knows. podcast-recommendations fixes discovery:

• Randomized probe queries ("lesser known true crime", "history dispatches") fish the long tail instead of recycling the top 10.

• Hard filters remove dead shows, one-episode experiments, explicit content (opt-in), and anything on your exclude list — with publisher-level dedupe so multi-picks stay diverse.

• Feed peeks compute cadence (daily/weekly/…) and typical episode length server-side — commute-fit facts the model never has to calculate.

• Honest attribution: every pick says which probe surfaced it. Discovery you can trust.

Related MCP server: PodcastIndex MCP Server

Tools

Plus prompts: surprise-me, commute-pick.

Quickstart

# 1-Line Universal Installer (auto-configures Claude Desktop, Cursor, Claude Code, VS Code, ...)
curl -fsSL "https://podcast-recommendations.builditwithai.xyz/install" | bash

# Or run directly via your preferred runtime:
uvx podcast-recommendations
npx -y podcast-recommendations

Example

User:  find me a podcast about true crime I haven't heard of

roulette(topic="true crime", exclude=["Serial", "Casefile"])
→ picks: [{
     title: "Milk and Murder", episode_count: 23,
     cadence: "biweekly", typical_episode: "24m",
     why_picked: "surfaced by the probe query “true crime chronicles”…",
     feed_url: "https://www.spreaker.com/show/4529395/episodes/feed",
     recent_episodes: ["24. Lindsey Baum - Part Two (2022-05-11, 10m)", ...] }]

Telemetry & privacy

Anonymous usage telemetry (no PII, no queries, no paths) via the fleet standard (schema v2, dual-endpoint fallback). Opt out any time: PODCAST_RECOMMENDATIONS_TELEMETRY=false or DO_NOT_TRACK=1.

Development

uv venv && uv pip install -e ".[dev]"
DO_NOT_TRACK=1 .venv/bin/python -m pytest tests/ -q   # unit + live + e2e

Live tests hit the real iTunes endpoints and real podcast feeds; they skip themselves when offline.

License

MIT