"Cool MCP (Model Context Protocol) tools for DevOps" matching MCP servers:

Summarizes Slack activity and GitLab merge requests with OpenAI to help generate daily standups, weekly reports, or quarterly check-ins.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

Provides a suite of regex and text processing tools for AI agents, including pattern testing, extraction, and replacement with capture group support. It also enables various text transformations like case conversion, line sorting, and deduplication through the Model Context Protocol.

MCP stdio server that exposes 95 headless it-tools utilities (crypto, encoding, converters, generators, text, math, and more) through three routing tools (categories, list, execute), with flat JSON schemas, worked examples, and deterministic seeded execution designed for small router models.

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.

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.

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.

Enables AI assistants to search YouTube, fetch transcripts, and get AI summaries of videos without API keys.

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.

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

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

Compresses, deduplicates, and filters tool outputs to optimize token usage and context window for AI agents.

The local PDF workflow for Claude Desktop and MCP hosts: fill, sign, merge, split, extract, and analyze PDFs without sending files to a web app.

Provides a bulk_read_files tool that reads specified files and forwards them to a remote backend for summarization, so agents receive concise summaries instead of raw file contents.

A simple notes MCP server that enables creating, listing, and summarizing text notes via resources, tools, and prompts.

An MCP server that provides developer knowledge and utility tools including local documentation search, text summarization, safe arithmetic calculation, unit conversion, and knowledge-base resources.

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

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.

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.

Enables AI assistants to save, load, and search conversation context with AI-powered summarization and auto-tagging. Demonstrates semantic intent patterns and hexagonal architecture for maintainable AI-assisted development.