"Super User" matching MCP servers:

An MCP server that provides a queryable, agent-native layer over Israeli supermarket price transparency feeds, enabling Hebrew product search, online delivery optimization, and cross-chain price comparison.

Perplexity AI search, reasoning, research, and compute - MCP server, dashboard, and multi-IDE auto-config for VS Code.

User-owned AI memory layer exposing persistent memory, task graph, and semantic search via MCP server, with data stored on DWN under user control.

Serves prompt templates through a standardized protocol for transforming basic user queries into optimized prompts for AI systems.

Enables interaction with the Rezoomex API for project management and user story analysis. Provides real-time access to user stories, personas, project overviews, and user journeys with SSE support and secure bearer token authentication.

Enables AI agents to perform offline cybersecurity research and penetration testing by querying a local knowledge base of curated security data, with tools for searching, answering, and status checking.

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.

Persistent memory for AI agents. Store, recall, and share knowledge across sessions with five MCP tools: remember, recall, context, forget, and share. Includes semantic search and agent/user/org scoping.

This MCP server provides comprehensive Google Workspace integration (Gmail, Drive, etc.) with multi-user OAuth. Unique features include RAG-powered natural language to Chat card rendering, runtime Jinja2 macro creation, and dynamic tool management.

Provides brain-inspired long-term memory for MCP clients, enabling persistent user understanding across sessions with Chinese-optimized hybrid retrieval and evolving user profiles.

An enterprise-grade data analysis system that enables users to discover data sources, configure analyses, and execute workflows through Claude Desktop. It features intelligent caching, session isolation, and secure JWT authentication for streamlined multi-user data orchestration.

Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.

An MCP server that allows users to efficiently search and reference user-configured documents through document listing, grep searching, semantic searching with OpenAI Embeddings, and full document retrieval.

An enhanced MCP server that enables users to interact with NotebookLM while ensuring strict source fidelity and citation accuracy through client-side prompt structuring. It guides Claude to transform user queries into structured prompts that prevent the addition of external knowledge during document analysis.

Provides persistent, decay-aware memory for AI applications by integrating the Smara Memory API through semantic search and Ebbinghaus forgetting curves. It enables tools to store, search, and manage long-term user context with importance-based ranking and automatic decay.

Enables AI applications to add, search, update, and delete long-term memories using the Mem0 Memory API, allowing agents to persistently remember user preferences, conversation history, and contextual information across sessions.

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.

Secure Edge Gateway for Data Normalization.

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.

With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding, keeping context across sessions without any SDK integration. With MCP, it can: Store stable user facts and preferences after answering using the advanced_augmentation tool Recall relevant memories before answering using the recall tool Maintain context across sessions us