"Information about cursors (general use or specific context)" matching MCP servers:

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.

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.

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

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

This server provides a note storage system with a custom URI scheme, allowing users to add and summarize notes, with adjustable summary detail levels.

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

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

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.

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 long-horizon autonomous agents to avoid context-window exhaustion by enforcing configurable token budgets and distilling older conversation turns into concise sliding summaries.

Enables autonomous agents to keep long-running conversations within configurable token budgets by compacting message arrays, preserving system instructions and recent turns while distilling older history into concise summaries. This prevents context window exhaustion without external LLM dependencies.

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.

Fewer tokens, every page. A local MCP server that turns web pages, PDFs, Office files and JSON into compact text for your coding agent.

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

Enhances and cleans raw prompts using AI to make them more clear, actionable, and effective. Provides quality assessment, suggestions, and supports both general and code-specific optimization modes.

Provides clean, bounded web content to AI by filtering HTML noise and optionally summarizing, preventing context window overflow.

Model Context Protocol server for Archive of Our Own: search fanfiction and have an AI read and rank fics for you.

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Self-hosted digests of your Telegram channels over MCP: AI summaries, the last digest and raw per-channel messages. Works with OpenAI, Anthropic or local Ollama.

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.

An MCP server that provides local, private, synchronous access to Ollama models for prompt answering, text classification, and model listing, without any file access or command execution.

A Model Context Protocol server that distills documents from multiple sources into HTML articles and Obsidian notes, with features like key element detection, image filtering, and async task management.

MCP server for RSS aggregation and LLM summarization, allowing users to fetch latest articles, search archives, and generate topic-specific digests via natural language.

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.

An MCP (Model Context Protocol) server specialized in deciphering PDF internal structures.

Model Context Protocol server exposing the bankstatementparser library as first-class agent tools for reading bank statements.

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 AI agents and MCP clients to browse Roomtone meeting notes, add YouTube or pasted-text sources, generate summaries, ask questions answered from transcripts, and retrieve raw transcript text. Recording itself stays in the Roomtone app, so the server only reads and adds to existing meetings.

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.

Enables context compression for Claude Code with Thai language support, reducing token usage by 50-88% while preserving full recoverability of original content.