"Information about Supabase Platform Features" matching MCP servers:

A TypeScript-based MCP server for handling a simple notes system with NextJS that supports creating and summarizing text notes using MCP concepts.

A modular production-ready system that provides specialized agents for math, research, weather, and summarization tasks through a unified MCP toolbox with smart supervisor capabilities.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

A Model Context Protocol server that distills documents from multiple sources into HTML articles and Obsidian notes, with features like key element detection, image filtering, and async task management.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

Generates natural, fluent Chinese content that reads like human writing using the Qwen3-Next-80B-A3B model. Supports web search for latest information and direct file saving with customizable writing rules.

An MCP server that converts PDF files to Markdown format using AI sampling capabilities, supporting both local files and URLs with incremental conversion features.

Analyzes YouTube videos using Google's Gemini API, allowing users to get summaries or ask questions about video content via direct URL input.

Provides MCP tools for querying and summarizing Northwest A&F University campus information, including trending QQ channel posts, official website notices/events/competitions, and cross-site custom searches with Markdown output and source links.

Enables LLM agents to search arXiv, download papers, parse PDFs into structured sections, and extract key findings using client-side LLMs. Features persistent caching and layout-aware PDF extraction.

Extracts transcripts or spoken captions from multi-platform video links (Bilibili, YouTube, Douyin) and returns concise Markdown summaries or full transcripts, saving tokens by keeping full text out of the conversation.

distill-mcp-v2 is a high-performance, network-dependency-free Python FastMCP server designed to aggressively optimize Large Language Model (LLM) context windows. It provides specialized tools for compressing and analyzing massive AI-agent payloads without losing critical semantic information.

MCP server for Glanceway, the macOS menu bar information app, enabling AI assistants to read sources and items.

Enables AI assistants to read, search, create, and organize notes in an Obsidian vault, with features like summarization and daily most valuable notes identification.

Enables complex queries over data on the DANDI Archive and provides a system for managing text notes. It features tools for note creation, resources for note access via custom URIs, and prompts for generating summaries.

Extracts and summarizes learning content from YouTube videos, PDFs, and web articles to provide context for project-based learning. It features automated background processing and integrates with Forest's HTA builder for informed task generation.

Unofficial MCP server wrapping crawl4ai that enables extraction and analysis of content from web pages, PDFs, Office documents, YouTube videos, and more, with AI-powered summarization and Google search integration to reduce token usage while preserving key information.

A comprehensive Model Context Protocol server for content summarization that supports web scraping, file reading, content summarization, and topic-based summarization features.

Enables LLMs to retrieve and analyze Steam game reviews, providing access to review statistics, game information, and helping summarize pros and cons of games.

Wraps the gpt-researcher tool as an MCP server, enabling automated web research, information summarization, and structured report generation via natural language queries.

A Model Context Protocol server that integrates with the Acrolinx NextGen API to provide advanced text analysis and improvement capabilities to AI assistants like Claude, offering features such as text rewriting, content analysis, and writing suggestions.

Enables local-first logging of daily activities and generation of daily summaries, weekly article ideas, and platform-specific social posts via Claude, storing everything in a single SQLite file.

A Model Context Protocol server that enables Claude to fetch, process, and extract information from PDF documents, including LaTeX mathematical equations.

Enables users to turn household paperwork into answers about upcoming deadlines, warranties, and plain-language explanations, and to draft related messages such as payment extension requests.

Enables AI language models to interact with YouTube content through a standardized interface, providing tools for retrieving video information, transcripts, channel analytics, and trend analysis.

Enables natural language querying of Microsoft Fabric Data Warehouses with intelligent SQL generation, metadata exploration, and business-friendly result summarization. Features two-layer architecture with MCP-compliant server and agentic AI reasoning for production-ready enterprise data access.

A note management MCP server that enables users to store, retrieve, and summarize personal notes via a custom URI scheme. It provides tools for adding content and prompts for generating brief or detailed summaries of stored information.

Enables parsing of raw resume text into structured JSON format with organized sections for skills, experience, education, and projects. Uses Google's Gemini AI model to extract and categorize resume information from unstructured text input.

A TypeScript-based MCP server that enables users to manage text notes with features like note creation and summary generation using a Model Context Protocol.

Local MCP server that manages Gmail OAuth 2.0 authentication (read-only) and filter setup, with planned AI-driven email digest and summarization features.