"A server for finding information and resources related to UX (User Experience) design" matching MCP servers:

Provides a cross-session experience knowledge base for AI agents, enabling retrieval of past problem-solution experiences via MCP tools. It helps agents reuse solutions without retraining, with zero-LLM runtime retrieval.

Enables AI agents to persistently recall, apply, and reinforce solutions to previously solved problems across different models and tools, so mistakes are not repeated.

Stores photos and notes as experience memories in Google Drive and enables natural language search to retrieve them later.

Central user-state database MCP server with durable profile facts and live virtual sensors (stress, room intensity) with configurable smoothing, readable by any agent or smart-home service.

MCP server bridging ChatGPT-compatible clients to the Design Flow System, enabling project session management, draft import, round locking, and read-only access to design state and traceability.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Provides source-backed design context, route card validation, contract generation, critique and verification reports, evidence packages, Penpot change plans, and anti-repeat checks for design workflows. Does not directly mutate Penpot, but consumes read-only Penpot snapshots.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables MCP-capable agents to retrieve relevant slices of a user-story knowledge graph (stored in Supabase + pgvector) using three retrieval verbs: find_related, find_crossover, and query_stories.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Provides a semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.

Enables AI agents to interact with application data as a specific user or agent while enforcing PostgreSQL Row Level Security, providing bounded tools for memory search, retrieval, and canonical mutation with an approval workflow.

A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.

Exposes a developer's local context (communication preferences, stack, repos, memory models) to AI agents via MCP tools and resources, enabling them to bootstrap with local guidelines and reduce context hallucination.

MCP server for Obsidian vaults, optimised for Cursor agents — safe note editing, lean tool surface, direct filesystem access.