"Information about Snowflake Data Warehouses" matching MCP servers:

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.

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 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.

Enables LLMs to interact with Fathom.video API for managing meeting recordings, retrieving transcripts and AI-generated summaries, searching meeting content, and accessing analytics and team data.

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.

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.

Enables fetching and analyzing YouTube video comments and replies through the YouTube Data API. Supports sorting by relevance or time and returns structured JSON data for comment analysis, summarization, and translation tasks.

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.

An MCP server for intelligent YouTube video analysis that provides token-optimized summaries, sentiment analysis, and entity extraction from transcripts. It enables AI assistants to perform video reporting, channel monitoring, and comprehensive YouTube searches through structured data tools.

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 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.

MCP server that connects AI assistants to ReviewWebsite.com API for creating and managing website reviews, extracting data, converting URLs to markdown, and interacting with web content.

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 server providing access to news articles from various categories including tech, data science, cybersecurity, and more, allowing retrieval and summarization of latest content.

MCP server providing 11 pay-per-call web intelligence tools (page reading, PDF extraction, RSS parsing, screenshots, summarization, structured data extraction) for AI agents, paid automatically in USDC via the x402 protocol on Base mainnet with no API keys required.

A local-first, open-source MCP server that analyzes jobs, matches your CV, tailors documents, and tracks applications — all on your machine with no data uploaded.

A text processing workbench and MCP server that provides 22 tools for cleaning, transforming, and extracting data from text. It enables AI assistants to perform complex operations like regex extraction, log normalization, and encoding through a unified interface.