MCP RSS Aggregator

Overview

The MCP RSS Aggregator is a lightweight, Model Context Protocol (MCP) server that bridges the gap between traditional RSS feeds and modern AI assistants such as Claude Desktop. By exposing a simple, well‑structured API, it allows an assistant to retrieve, parse, and present up-to-date news articles from any number of sources without requiring the user to leave the conversational interface. This solves a common pain point for developers and power users: keeping an AI assistant informed with real‑time, curated content from their preferred outlets while maintaining a clean and consistent workflow.

At its core, the server reads feed definitions from an OPML file—an industry‑standard format that many RSS readers export—and converts each subscription into a stream of article objects. These objects include the title, snippet, publication date, and link, all formatted for optimal readability inside a chat window. The server also supports JSON feed configurations, giving developers flexibility to embed custom feeds directly into their applications. Once configured, the MCP exposes a single resource endpoint that returns all recent items across every feed, optionally filtered by category or source. This unified view enables developers to build “news‑reader” experiences, trigger follow‑up queries based on headlines, or even schedule periodic updates for a continuous knowledge base.

Key capabilities include:

• Category organization – Feed entries are tagged with user‑defined categories, allowing the assistant to filter or sort news by topic (e.g., technology, politics, sports).
• OPML import – Existing subscriptions can be imported wholesale, making it trivial to migrate from any popular RSS reader.
• Real‑time aggregation – The server polls feeds at configurable intervals, ensuring that Claude always has the latest articles without manual refreshes.
• Well‑formatted presentation – Each article is rendered with a headline, short excerpt, and direct link, giving the user a clean reading experience within the chat.

Typical use cases span from personal productivity to enterprise analytics: a developer might integrate the aggregator into a workflow that auto‑generates daily briefing cards, while a content marketer could use it to surface trending topics for prompt engineering. In research settings, the server can feed an AI with a steady stream of academic or industry updates, enabling continuous literature reviews. Because the MCP interface is declarative and stateless, it can be composed with other MCP servers—such as a summarization tool or a sentiment analyzer—to create rich, multi‑step pipelines that keep the assistant’s knowledge current and contextually relevant.

What sets this aggregator apart is its focus on simplicity and interoperability. By leveraging OPML, it taps into a vast ecosystem of existing feed subscriptions, eliminating the need to manually curate URLs. Its single‑resource design means developers can expose all news content through one MCP endpoint, while the optional category filtering gives fine‑grained control without complicating the client side. For developers building AI‑powered applications, this server offers a turnkey solution to keep assistants informed with the latest external content, enhancing both user experience and the assistant’s effectiveness in dynamic information environments.