"Methods to Store and Learn Dynamic Context Memories" matching MCP servers:

An MCP server that gives desktop AI apps access to your Waterloo LEARN courses, enabling listing courses, getting announcements, content, grades, and upcoming events through natural language.

MCP server for managing and controlling multiple isolated Chrome/Edge browser instances, exposing tools for navigation, screenshots, clicking, form filling, and JavaScript execution to AI clients.

Provides live web and document intelligence, including search, company news, scraping, crawling, structured extraction, document parsing, brand intelligence, screenshots, website monitoring, and batch jobs.

MCP server that scrapes app data from Google Play and Apple App Store, providing tools for search, details, reviews, and similar apps.

Enables querying and retrieving data from App Store and Google Play Store, including app details, reviews, ratings, rankings, permissions, and search capabilities across both iOS and Android platforms.

A collection of self-hosted, open-source tools that enable AI agents to perform web searching, content crawling, and code analysis tasks like linting and security scanning. It provides power-user capabilities for MCP-compatible clients without requiring external accounts or third-party infrastructure.

Local-first documentation for AI agents

An MCP server that fetches web pages and extracts clean, AI-usable context from them, enabling tools for link discovery, content search, and integrated fetch-and-search operations.

Enables AI assistants to summarize, take notes on, and answer questions about YouTube, Bilibili, and Xiaohongshu videos by providing subtitles and local speech transcription with timestamps via MCP.

An MCP server that automates converting diverse content sources like WeChat articles, YouTube videos, and various document formats into AI-generated outputs such as podcasts and slide decks via Google NotebookLM. It integrates specialized tools for web scraping, OCR, and file transformation to facilitate seamless content generation through natural language.

Enables converting webpages into structured JSON using CSS-selector schemas, with anti-bot handling and support for protected pages.

Enables converting any webpage to clean LLM-ready markdown, with extraction, article, and news modes, using a real browser with anti-bot handling.

Fetches a URL and returns the main content as clean Markdown, using plain HTTP when possible and headless Chromium for JavaScript-rendered or bot-protected pages.

Enables converting a public release-note URL into ordered upgrade tasks with source excerpts, anchors, confidence, and uncertainty, without executing release text or project code.

Fetches and caches public site pages from llms.txt and returns structured answers with source URLs, enabling verification of claims against live public content.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

Fetches website content and converts it to Markdown format with AI-powered content cleanup, ad removal, and full OpenAPI/Swagger specification support for easy processing by AI assistants.

Converts X (Twitter) posts into JSON, PDF, PNG, Markdown, or Slack/Discord messages using public endpoints, no API key or login required.

MCP server that fetches remote or local HTML resources and converts them to Markdown, enabling AI clients to summarize or process web content.

A read-only MCP server that gives AI agents the web as compact, ranked, verified evidence — no API keys, no cloud retrieval, all models local.