"A YouTube MCP server with statistics, available via npx or Python command" matching MCP servers:

Enables routing context and execution across AI tools like Claude, Cursor, Windsurf, and ChatGPT with a shared memory, task board, and context bus, plus local file conversion.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Enables AI assistants to query a company's inventoried productivity tools and subscriptions through MCP, answering questions about what is paid for, which tools overlap in function, and what renews next quarter. Exposes read-only tools such as list_stack, get_tool_context, find_overlap, and stack_summary over the local inventory file as typed context packets.

A simple note-taking MCP server that allows adding notes and summarizing them via a prompt.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

A sophisticated AI-powered server providing intelligent, context-aware conversational capabilities with role-based advisors, semantic memory, multi-LLM support, and web browsing.

Transforms YouTube into a queryable knowledge source with search, video details, transcript analysis, and AI-powered tools for summaries, learning paths, and knowledge graphs. Features quota-aware API access with caching and optional OpenAI/Anthropic integration for advanced content analysis.

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.

Enables AI agents to verify Ceph CLI commands, config parameters, and review test scripts using a pre-generated knowledge base.

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.

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.

A Python-based server providing persistent memory management for AI models with SQLite and Markdown dual backend storage. It features full-text search, RAG-enhanced querying, and cross-project knowledge sharing for integration with Claude, Cursor, and Rovo Dev.

Builds a searchable knowledge base from YouTube video transcripts with hybrid semantic and keyword search. Allows LLM assistants to search, organize, and retrieve timestamped information from videos you've watched.

Memory bank with Server as SSH support for central knowledge base

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

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

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Connects Bear Notes to AI assistants using semantic search and RAG (Retrieval-Augmented Generation), allowing AI systems to access and understand your personal knowledge base through meaningful search rather than just keyword matching.

A FastAPI server that implements the Model Context Protocol (MCP) using Server-Sent Events (SSE) transport to provide random cat facts on demand or as a continuous stream.

An MCP server that enables Claude to interact with Google NotebookLM via browser automation, allowing management of notebooks, adding sources, and querying information.