podcast-recommendations
Podcast Recommendations — Podcast Discovery MCP 🎰🎙️
超越排行榜的播客推荐:长尾播客发现、推荐与随机抽选——随机化的探测查询会浮出那些榜单永远循环之外的节目,而每一个选择都会附带它的 feed URL、更新频率与典型单集时长。
让任何智能体问:"帮我找一档我没听过的拜占庭历史播客" 或 "给我个播客惊喜" —— podcast-recommendations 就是它的答案。零 API 密钥,零配置。 它基于 Apple 的免密钥 iTunes 播客接口(已验证)和 RSS feed 直读构建:一段 300KB 的 feed 变成约 200 tokens 的节目情报,且所有计算都在服务端完成。
为什么做这个
搜索 API 对每个查询都会回同一批排行榜榜首;LLM 推荐来推荐去也就是那些众所周知的播客。podcast-recommendations 修复的正是发现环节的问题:
随机化探测查询(如"小众真实犯罪"、"历史快讯")钓出来的是长尾内容,而不是转个不停的 Top 10。
硬过滤去掉停更节目、只出一期的试验品、不适宜内容(可选择加入),以及排除列表中的一切——并在来源层级去重排重,让多次挑选保持差异度。
Feed 速览在服务端计算出更新频率(日更/周更/…)与单集典型时长,这些适合通勤场景的事实无需模型去算。
诚实的归属说明:每一次选品都会标明它是由哪一个探测查询驱动的。这是你可信任的发现体验。
工具
工具 | 作用 |
| 发现轮盘:按主题(或完全随机)进行长尾选品,返回 1–5 档不同的节目 |
| Apple 整体榜单或按分类榜单,附加 feed URL 与集数信息 |
| 读取任意播客 RSS feed → 得到更新总频率、典型长度、最新单集 |
|
|
| 可更新的使用手册(运行时从本仓库获取) |
另有提示词:surprise-me、surprise-me。
快速开始
# 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示例
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)", ...] }]遥测与隐私
匿名使用遥测(不含 PII、不记查询、不收集路径数据),遵循 fleet 标准(schema v2、双端点回退)。随时可以退出:PODCAST_RECOMMENDATIONS_TELEMETRY=false 或 DO_NOT_TRACK=1。
开发
uv venv && uv pip install -e ".[dev]"
DO_NOT_TRACK=1 .venv/bin/python -m pytest tests/ -q # unit + live + e2e实时测试会访问真实 iTunes 接口和真实播客 feed;离线时它们会自行跳过。
License
MIT
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