"Using or Operating a Screen" matching MCP servers:

Provides LLMs with persistent memory across conversations using a knowledge graph that stores entities, relationships, and observations with support for PostgreSQL or SQLite backends.

Enables Claude to remember and search everything that appeared on your screen using local OCR and embeddings. Provides tools for semantic search, activity timeline, and code change tracking.

Connects to SlimSnap to provide recent screen captures as structured JSON data (text and annotations) for AI to read, avoiding screenshot limitations.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

A production-oriented Agent Operating System kernel that provides multi-graph memory, durable tasks, portable skills, attestation, and plan-based monetization for MCP-based agents.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Enables Claude AI to seamlessly interact with Scrivener projects for document management, AI-powered content analysis, and advanced writing assistance.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Extends large language model context length up to 60 times using gravitational memory, designed for Claude Code and Anthropic AI models.

Extends LLM context windows by 15-60× using gravitational memory compression, enabling massive context extension for codebases, research papers, and long documents.

A lightweight, local-first MCP server and CLI that helps developers and AI coding agents store and retrieve lessons learned during development, using SQLite and context-aware filtering.

Local-first memory for AI coding agents using SQLite, enabling persistent recall of decisions, lessons, and project context without cloud or API keys.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.

A shared memory MCP server for the Senegalese MOHEBS teaching and learning materials pipeline that ensures AI-generated documents remain consistent in characters, terminology, and concept coverage while varying example domains, operating across grade and subject via Firebase storage.

Serves as a machine perception and action hub, enabling heartbeat monitoring, screen/window/clipboard sensing, todo management, long-term memory, and web/file operations through both CLI and MCP interfaces.

A local, zero-cost MCP server that fact-checks your Markdown/Obsidian notes using a local LLM (via Ollama) and free sources like Wikipedia and DuckDuckGo, with no cloud API or API keys.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

Personal multi-LLM memory repository using Markdown as source of truth, SQLite FTS5 for retrieval, and MCP tools for search, context, and write proposals.