"Designing UI (User Interfaces)" matching MCP servers:

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.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

Serves UltraRAG memory to agents over stdio MCP, providing project-local and per-user global memory. It lets agents read and append standing memory and daily dialogue rounds in UltraRAG's format, with an optional browser view.

An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

Enables Claude Desktop and compatible agents to search, read, add, update, and delete a locally stored, user-curated memory using hybrid retrieval with reranking and Qdrant. All processing runs locally without cloud services or API keys.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.

Provides LLM clients with a persistent, scalable knowledge graph memory system that supports semantic retrieval, contextual recall, and temporal awareness.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

Enhances large language models with competitive programming knowledge by leveraging OI-Wiki content through vector search, allowing models to retrieve relevant algorithms and techniques.

Persistent memory MCP server for Claude Code — self-hosted, n8n + PostgreSQL + pgvector. Team memory for AI agents with multi-user roles, multi-project namespacing, and hybrid vector + keyword search. No cloud required.

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with persistent storage and semantic similarity matching.

MCP Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

A self-evolving RAG system that enables AI agents to autonomously read and write memory, continuously learning and adapting user preferences, daily logs, and knowledge graphs across applications.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.