"A powerful filesystem that works on both Windows and Mac" matching MCP servers:

A TypeScript-based MCP server that implements a simple notes system, allowing users to create, access, and generate summaries of text notes via URIs and tools.

A simple note storage system that lets users create and summarize notes with varying detail levels using Claude.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Enables exploring Steam store reviews and community discussion threads with server-side filtering, temporal analysis, and metadata context for understanding player sentiment and feedback beyond store page noise.

Fetches category data from a Q&A service API and returns a markdown summary grouped by category.

Enables interactive AI chat through the Anthropic API, with document retrieval, slash commands, and extensible MCP tool integrations.

Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.

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.

MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.

Enables transcription, summarization, and action item extraction from audio files on your Mac using MacWhisper and Claude Desktop, all locally without any cloud APIs.

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

A powerful MCP server for summarizing YouTube videos with transcript fetching, intelligent summarization, key point extraction, and metadata retrieval.

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.

Enables reading and extracting content from PDF documents including text (as Markdown), images, tables, and metadata from both local files and URLs, with OCR support for scanned documents.

Enables fast text classification using Model2Vec static embeddings with 10 default categories (technology, business, health, etc.), supports custom category management, batch processing, and provides both local and remote deployment options.

Provides powerful Markdown document editing capabilities with thread-safe operations, atomic transactions, and comprehensive validation.

A TypeScript-based MCP server that implements a simple notes system, allowing creation and management of text notes with URIs and metadata.

A Node.js implementation of the Kagi Model Context Protocol server that enables Claude AI to search the web and summarize documents, videos, and audio using Kagi's APIs.

A simple note storage MCP server that allows adding notes and summarizing them through prompts.