"How to fetch or scrape data from a website for use in training an LLM" matching MCP servers:

Persistent memory and pattern learning for AI coding assistants via MCP.

Serves an LLM-maintained markdown wiki to agents over MCP, providing tools for querying and navigating wiki pages without direct filesystem access.

An MCP server for a plain-markdown LLM wiki vault with YAML frontmatter and Obsidian-style wiki-links, enabling headless full-text search, note reading, backlink discovery, and safe append/create-only writing with path traversal protection and an immutable raw/ directory.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.

A local-first memory cartridge for MCP-compatible AI clients, providing durable, inspectable, and auditable memory storage with a read-only-by-default MCP server.

Graph-level operations for Karpathy LLM Wiki knowledge bases, enabling LLM agents via MCP and humans via CLI to perform structured graph surgery including node/edge CRUD, wikilink management, index rebuilding, and graph metrics.

Private knowledge base with Markdown pages and assets, exposed via MCP for agent access.

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 LLM clients to read, search, create, update, and delete notes in a self-hosted shared memory bank over MCP, using hybrid vector and full-text search with optional summarization.

MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.

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.

Persistent markdown wiki for your AI agent, built on Karpathy's LLM wiki gist.

Builds a local, citation-aware wiki from an approved project scope and exposes it as an MCP server with a single read-only tool for agents to explore the wiki.

Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.

Enables building and querying a local wiki knowledge base from raw source files via MCP tools and a PyQt viewer.

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.

Enables natural language query and ingestion of wiki knowledge bases built from documents, powered by Claude Agent SDK skills.

A KISS self-hosted wiki server for AI agents, providing MCP tools to list, read, search, write, and append notes to Markdown files on the filesystem.

An MCP server providing RAG and CAG capabilities for querying and augmenting BambiSleep knowledge base data.

An MCP server that provides AI agents with a governed wiki requiring human approval before any formal page update, combining structured knowledge management with safe, auditable writes.

A local-first MCP server that enables MCP hosts to use GLM-family LLM providers for knowledge consultation, reasoning, UI auditing, repository inspection, and approval-gated development assistance.

A TypeScript-based MCP server that implements a simple notes system, allowing users to create, access, and generate summaries of text notes through Claude Desktop.

This server provides a note storage system with a custom URI scheme, allowing users to add and summarize notes, with adjustable summary detail levels.

Production-ready MCP server enabling LLM text generation, template management, context-aware conversations, and memory storage with enterprise quality assurance.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

MCP server for persistent, compounding markdown wikis maintained by LLMs. Enables incremental knowledge base building with interlinked pages, search, and raw source management.

Enables Claude to automatically extract entities and relationships from URLs, PDFs, and YouTube videos to build structured knowledge graphs in Neo4j. It supports custom schemas, academic citation extraction, and community detection for advanced research and content analysis.

Enables AI assistants across platforms to share memory, recall facts, preferences, and conversation history, creating a unified cognitive system.

A generic MCP server that turns a directory of Markdown wiki pages into a network-reachable knowledge base, queryable and updatable by any MCP-compatible client.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.