"Using Pollinations to Create Images Without Logos" matching MCP servers:

MCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.

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.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Enables natural language management of Gmail through MCP tools for searching, analyzing, summarizing, drafting, and sending emails, with AI reasoning and user confirmation for actions.

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

Search and discover YouTube channels via natural language, track them, and summarize videos without transcripts using Gemini API.

Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.

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.

Allows AI agents to fetch, search, and summarize YouTube video transcripts without an API key, including chapter summaries.

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.

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.

Enables natural language interaction with local .docx files, allowing users to find, read, search, and summarize Word documents using friendly names and location hints.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.

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

Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.

MCP server for HumanPen that lets AI agents work on real documents (.docx, .pptx, .pdf) — humanizing content to lower AI-detection scores, converting citations, condensing, and translating while preserving formatting, tables, images, and citations.

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 any LLM to process arbitrarily long contexts through recursive decomposition, without requiring external LLM APIs.

Enables users to extract and review Claude's plan reasoning in Claude Code by reading plan files and providing a structured template for decisions, tradeoffs, and assumptions.