"A tool or guide for creating a memory card game" matching MCP servers:

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

A fully-local AI memory system that saves notes, code, and terminal output to a vector database via Ollama and LanceDB, enabling context retrieval from terminal, API calls, or inside Claude Code.

A local-first MCP server for persistent memory with vector search, metadata filtering, fact tracking, and graceful degradation when dependencies fail.

A production-ready MCP server for persistent AI memory across LLMs like Claude and ChatGPT. Provides automatic conversation backup, multi-user support, and multi-storage (PostgreSQL, Redis, Qdrant).

MCP Memory is a MCP Server that gives MCP Clients the ability to remember information about users across conversations using vector search.

An MCP server that gives an Agent Studio project a memory that outlives a run, using S3 for storage and exposing tools to recall, remember, list, and forget memories.

A self-hosted MCP memory and state system for long-running AI relationships, storing source text locally with explicit memory tools and a web manager for humans.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

A long-term memory MCP server for AI companions that lets them store what the user says, let memories fade by importance, emotion and time, and automatically surface relevant ones through semantic recall, people cards, dates, letters and sticky notes. It also provides hybrid vector-plus-keyword search and memory management tools (wake, recall, remember, update, pin, forget) over stdio or authenticated HTTP with a Docker/Postgres backend and web memory globe.

A persistent, conflict-aware memory MCP server for AI coding assistants (Cursor, Claude Code).

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

Local persistent memory for AI agents with recall, semantic search, forgetting, and encrypted storage.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.

A privacy-first memory layer for AI that securely bridges local knowledge with AI assistants through MCP tools, with intelligent redaction and local vector database.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

Enhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.

MCP server for semantic code indexing using vector embeddings, enabling AI agents to maintain persistent memory of codebases through natural language queries and intelligent chunking.

A portable, self-hosted semantic memory layer for LLM agents, enabling them to search and add memories by meaning over the Model Context Protocol.