"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.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

Enables AI agents to browse, search, and read a curated library of 198 UI/UX and animation design skills across 17 categories, distilled from 10 open-source projects, with a progressive 5-step workflow for guidance.

Enables AI-powered access to authoritative design systems knowledge, including W3C standards, WCAG guidelines, and best practices from 188+ curated entries via semantic vector search.

An MCP server that enables AI agents to semantically retrieve premium UI components from Awwwards and adapt them into clean React + Tailwind code.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

A local-first FastMCP server that indexes offline frontend catalogs (components, patterns, motion libraries) and exposes discover, search, compare, and recommend tools to AI agents via stdio.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

A proof of concept MCP server for Apache OFBiz

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

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.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

A read-only MCP server that enables semantic search over a Chroma library of narrative and game design content, using multilingual embeddings (BAAI/bge-m3) and offering tools to query indexed books, retrieve page chunks, and list available titles.