"Information about pages or the term 'page'" matching MCP servers:

A persistent, human-like memory system for AI companions in LM Studio, using SQLite and ChromaDB for hybrid storage, enabling seamless recall across sessions and models.

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

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.

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

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

Gives AI agents private, persistent, semantically searchable long-term memory with RAG, governed multi-agent scopes, and real-time memory-change subscriptions.

A personal memory system that provides AI assistants with long-term memory capabilities through semantic search and vector storage. It enables Claude Code to store, retrieve, and manage personal context and project preferences using flexible LLM backends.

A long-term memory MCP server for AI agents that stores memories (facts, decisions, etc.) in a single SQLite database with hybrid search and full edit history, ensuring consistency across sessions.

Provides long-term memory storage for AI assistants with semantic search, enabling persistent storage of preferences, decisions, and context with relationship tracking between memories.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

A TypeScript-based MCP server that provides project-specific knowledge graph memory for LLM agents to store and retrieve entities, relations, and observations. It features disk-persistent storage and supports cross-project knowledge sharing to enhance agent long-term memory.

VecFS is a lightweight, local-first vector storage specification and implementation for AI agent long-term memory. It provides an MCP server enabling agents to search, memorize, and manage context.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

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.

A persistent local vector memory server that allows users to store and search project-specific context using LanceDB and local embeddings. It enables MCP-compliant editors to maintain long-term memory across different projects without requiring external API keys.

An MCP server that provides persistent semantic memory for LLMs by building a concept graph with vector search. It enables storing, linking, and retrieving concepts across conversations using Turso vector search and 256-dimensional embeddings.

Enables persistent memory for AI systems by providing tools for episodic, semantic, and procedural data storage through a vector-and-graph-enhanced database. It allows models to maintain long-term continuity using similarity search, thematic clustering, and identity tracking.