RSS Reader MCP Server

The RSS Reader MCP server fills a common gap for developers building AI‑powered assistants: the ability to pull real‑time content from any public RSS or Atom feed without writing custom parsers. By exposing a single, well‑defined tool () the server lets an AI client request structured updates from a feed URL and receive a JSON payload that already contains metadata, item lists, and any embedded media or custom fields. This eliminates the need for repetitive boilerplate code and guarantees consistent error handling across all integrations.

At its core, the server is a thin wrapper around the popular library. It issues HTTP requests with , enforcing a 10‑second timeout and validating the URL before attempting to fetch. Once the feed is retrieved, the parser normalizes a variety of formats—RSS 2.0, RSS 1.0 (RDF), and Atom—into a common object model. The output includes standard fields such as title, link, description, publication date, and author, as well as support for media enclosures, thumbnails, categories, and custom Dublin Core or content‑encoded fields. The structured JSON is ready for downstream consumption by AI workflows, enabling quick summarization, filtering, or content‑generation tasks.

Key capabilities that set this MCP apart include:

• Robust error handling: URL validation, HTTP status checks, network timeouts, and parsing errors are all surfaced with descriptive messages that aid debugging.
• Custom field extraction: Media content, thumbnails, and other non‑standard tags are preserved so AI assistants can surface rich media or perform advanced analyses.
• Unified format support: Whether the feed is classic RSS, RDF‑based RSS 1.0, or Atom, the server normalizes it into a single schema, simplifying downstream logic.

Typical use cases span a wide range of AI applications. A news‑curation assistant can call to fetch the latest stories from multiple feeds, then use natural‑language generation to summarize or translate them. A podcast recommendation bot can retrieve episode lists, parse enclosure URLs, and present playable links. Marketing tools might aggregate blog updates to trigger email campaigns automatically. In each scenario, the server’s deterministic output reduces integration friction and speeds time‑to‑value.

Integration into existing AI workflows is straightforward: the MCP client (e.g., Claude Desktop) declares the server in its configuration, and the assistant can invoke as part of a prompt or a chain of reasoning steps. Because the server operates over standard input/output, it can be run locally or hosted in a cloud function, providing flexibility for on‑premise security or scalable deployment. The combination of minimal setup, comprehensive parsing, and clear error reporting makes the RSS Reader MCP server a practical tool for any developer looking to enrich AI assistants with up‑to‑date, structured content from the web.