"Information about MCP Discovery" matching MCP servers:

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.

Integrates Vale prose linting into AI coding assistants, enabling users to check text files for style and grammar issues using Vale's powerful linting engine. Provides automated style feedback with smart configuration discovery and rich formatted results.

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 exposes tools to query Erick Wendel's talks, blog posts, and videos, supporting filters and pagination, plus prompts and resources for content discovery and summarization.

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.

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

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 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 production-grade MCP server providing four tools for URL summarization, content repurposing, news digests, and competitor analysis, with cost telemetry and dual transport support.

A hosted MCP server providing 15 media and data tools for AI agents, including web search, news, content extraction, summarization, translation, moderation, script writing, text-to-voiceover, transcription, subtitles, viral-clip discovery, and short-form video editing.

This MCP server performs multi-topic searches in business, news, finance, and politics using the Tavily API, providing high-quality sources and intelligent summaries.

Enables local tool calling over Model Context Protocol via stdio, providing deterministic tools such as calc.add, text.word_count, and text.summarize_naive after JSON-RPC handshake and discovery.

A Docker-containerized MCP proxy that provides AI image generation, text generation, vision analysis, and text-to-speech capabilities through REST endpoints using Pollinations AI services. Enables multimodal AI interactions including image creation, transformation, OCR, and audio generation through standard HTTP APIs.

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.

Facilitates note storage and summarization through custom URIs, allowing users to manage, summarize, and update notes with varying detail levels in a collaborative environment.

A Python-based MCP server adapted from the n8n_agent project that implements a note storage and summarization system. It enables users to create, retrieve, and summarize notes through the Model Context Protocol.

Enables arithmetic addition, personalized greetings, version lookup, and text summarization through MCP tools, a resource, and a prompt.