PodCrawlerMCP

PodCrawlerMCP is an MCP server that equips AI assistants with the ability to discover and curate podcast content by crawling the web for RSS feeds. In an era where podcasts are proliferating across niche topics, developers often struggle to surface relevant episodes without manual search or subscription to multiple directories. This server automates that process, turning a simple topic query into a structured list of episodes ready for downstream AI analysis or presentation.

The server performs three core tasks:

1. Web crawling – It navigates popular podcast directories and related sites, locating RSS feed URLs that describe individual podcasts.
2. RSS parsing – Once a feed is found, the server extracts episode metadata (title, description, publish date, audio URL) and normalizes it into a consistent format.
3. Topic filtering – Users can narrow results by specifying a topic keyword or domain, ensuring that the returned episodes are highly relevant to their query.

These capabilities are exposed through a single MCP tool, , which accepts a topic string and an optional maximum result count. The tool returns a list of episodes, each enriched with metadata that can be fed directly into conversational flows or content recommendation engines.

For developers integrating AI assistants like Claude, PodCrawlerMCP offers a plug‑and‑play solution: add the server to your MCP configuration, invoke in a prompt, and receive a curated list of episodes without writing any web‑scraping code. This streamlines workflows such as building podcast recommendation bots, generating episode summaries, or aggregating niche content for research.

Unique advantages of PodCrawlerMCP include its topic‑centric filtering—allowing precise control over content relevance—and its lightweight design, which keeps resource usage low while still traversing multiple directories. The server’s modular architecture (separate crawler, parser, and filtering components) makes it straightforward to extend with new directories or advanced NLP‑based relevance scoring. In short, PodCrawlerMCP turns the chaotic podcast landscape into a structured, AI‑ready data source that saves developers time and enhances user experiences.