"Building and Using MCP Tools" matching MCP servers:

MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.

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.

Enables analysis of clinical trial protocols using MCP tools for document listing, entity extraction, adverse event clustering, and summarization.

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

Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.

This TypeScript-based server implements a simple notes system using MCP concepts, enabling users to create, list, and summarize text notes through natural language prompts.

Content writing toolkit for headline generation, readability scoring, tone analysis, outline building, and plagiarism similarity checking.

An MCP server that enables users to read, search, and analyze PDF documents. It provides tools for extracting text, viewing metadata, searching content with context, and generating word statistics.

A TypeScript-based Model Context Protocol server that fetches, searches, and formats news articles from NewsAPI.org into reader-friendly Markdown digests using LLMs (Gemini and Claude).

Provides various AI capabilities through DeepInfra's OpenAI-compatible API including image generation, text processing, embeddings, speech recognition, object detection, and classification tasks. Enables users to access multiple AI models for diverse tasks like generating images from prompts, transcribing audio, analyzing text sentiment, and performing computer vision operations.

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.

An MCP server that converts files to Markdown using multiple parsing backends (markitdown, docling, LlamaParse) with automatic fallback, and includes tools for interpretation and chunking.

An AI-powered study assistant that generates quizzes, flashcards, summaries, and concept explanations from study materials using the Model Context Protocol.

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

This TypeScript-based MCP server allows users to manage and interact with a note system through Model Context Protocol, enabling note creation and summarization with URIs and metadata.

Provides intelligent manuscript analysis and writing assistance for markdown projects, including semantic search, quality checks, terminology consistency, link validation, progress tracking, and comprehensive writing statistics.

Enables intelligent web searching using SearXNG with content crawling via Creeper, then summarizes webpage content using LLM to avoid token limit issues. Supports smart filtering with domain blacklist/whitelist and optional LLM-based relevance filtering.

A server for managing and summarizing notes using a custom URI scheme, with tools to add notes and create styled summaries.

The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con