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RSS▌

missionsquad

by missionsquad

RSS feed server with intelligent caching, batch processing, content monitoring, and full‑text search for automated news

RSS/Atom feed server that provides intelligent caching, batch processing, content monitoring, and full-text search capabilities for news aggregation, content monitoring, and automated feed processing workflows.

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Built-in intelligent caching with TTLFull-text search across feedsMultiple output formats supported

best for

  • / News aggregation and monitoring
  • / Content curation workflows
  • / Automated feed processing systems
  • / Building RSS readers or news apps

capabilities

  • / Fetch and parse RSS/Atom feeds
  • / Batch process multiple feeds simultaneously
  • / Monitor feeds for new content since last check
  • / Search content across multiple RSS feeds
  • / Extract feed content in JSON, Markdown, HTML, or text formats
  • / Export feed subscriptions as OPML

what it does

Fetches and manages RSS/Atom feeds with intelligent caching, batch processing, and content search. Helps monitor multiple news sources and extract feed content in various formats.

about

RSS is a community-built MCP server published by missionsquad that provides AI assistants with tools and capabilities via the Model Context Protocol. RSS feed server with intelligent caching, batch processing, content monitoring, and full‑text search for automated news It is categorized under developer tools, productivity. This server exposes 6 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install RSS in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

Apache-2.0

RSS is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP-RSS Server

A Model Context Protocol (MCP) server for fetching, parsing, and managing RSS feeds.

Features

  • Fetch and parse RSS/Atom feeds
  • In-memory caching with TTL
  • Batch fetching of multiple feeds
  • Monitor feeds for new items
  • Search content across multiple feeds
  • Extract and format feed content
  • OPML export of subscribed feeds

Getting Started

  • Install: yarn add @missionsquad/mcp-rss or npm install @missionsquad/mcp-rss

Prerequisites

  • Node.js v20 or later
  • npm or yarn

Setup

  1. Install Dependencies: 
    yarn
  2. Configure Environment:
    • Copy .env.example to .env.
    • Edit .env and set the necessary environment variables.
  3. Build the Project: 
    yarn build
  4. Start the Server: 
    yarn start

Available Tools

  • fetch_rss_feed: Fetches and parses a single RSS feed.
  • fetch_multiple_feeds: Fetches multiple RSS feeds in parallel or sequentially.
  • monitor_feed_updates: Checks for new items in a feed since a specific time.
  • search_feed_items: Searches for content across one or more RSS feeds.
  • extract_feed_content: Extracts and formats content from feed items. Supports json, markdown, html, and text formats.
  • get_feed_headlines: Gets a list of headlines from a feed. Supports json, markdown, html, and text formats.

Available Resources

  • rss://cache/{feedUrl}: Access cached feed data.
  • rss://opml/export: Export all monitored feeds in OPML format.

Configuration

Configure the server using environment variables defined in .env. See .env.example for all available options.

Try it on Mission Squad

You can test the mcp-rss server and other MCP servers on the Mission Squad platform. Mission Squad is an Agentic AI Platform that allows you to build, manage, and deploy cooperative agents that connect to any model, leverage private data, and automate complex tasks. Sign up for a free account to get started

FAQ

What is the RSS MCP server?
RSS is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for RSS?
This profile displays 49 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases▌

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

✓

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

✓

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

✓

Complete complex tasks end-to-end without manual steps

Implementation Guide▌

Prerequisites

  • ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • ›Basic understanding of MCP architecture and capabilities
  • ›Access credentials for integrated services (if required)
  • ›Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • ⚠MCP server not loading: Check config syntax, verify installation
  • ⚠Connection errors: Check network, firewall, credentials
  • ⚠Feature not working: Read server docs, check required parameters
  • ⚠Performance issues: Monitor resource usage, check for network latency
  • ⚠Conflicts with other servers: Check port assignments, namespace collisions

Best Practices▌

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • −Don't grant overly permissive access to MCP servers
  • −Don't skip reading security considerations in docs
  • −Don't expose sensitive data without proper controls
  • −Don't run untrusted MCP servers without code review
  • −Don't ignore error messages—investigate root cause

💡 Pro Tips

  • ★Combine multiple MCP servers for powerful workflows
  • ★Create custom MCP servers for your specific needs
  • ★Share successful configurations with team
  • ★Use MCP inspector for debugging
  • ★Join MCP community for tips and troubleshooting

Technical Details▌

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This▌

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration▌

  • →Tool composition: Chain multiple MCP tools in workflows
  • →Context augmentation: Provide AI with relevant external data
  • →Action delegation: Let AI execute tasks on external systems
  • →Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.6★★★★★49 reviews
  • ★★★★★Yusuf Perez· Dec 28, 2024

    RSS is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • ★★★★★Yusuf Gonzalez· Dec 16, 2024

    I recommend RSS for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • ★★★★★Emma Sharma· Dec 12, 2024

    We evaluated RSS against two servers with overlapping tools; this profile had the clearer scope statement.

  • ★★★★★Sakshi Patil· Dec 4, 2024

    RSS is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • ★★★★★Chaitanya Patil· Nov 23, 2024

    Useful MCP listing: RSS is the kind of server we cite when onboarding engineers to host + tool permissions.

  • ★★★★★Evelyn Huang· Nov 19, 2024

    Strong directory entry: RSS surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • ★★★★★Naina Mensah· Nov 15, 2024

    RSS has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • ★★★★★Fatima Khan· Nov 7, 2024

    We evaluated RSS against two servers with overlapping tools; this profile had the clearer scope statement.

  • ★★★★★Arya Singh· Nov 3, 2024

    I recommend RSS for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • ★★★★★Naina Rahman· Oct 26, 2024

    RSS is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

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