"Methods to Scrape a Website and Fetch Markdown Files from GitHub" matching MCP servers:

Enables AI to search, retrieve, list notes, and get backlinks from a published Markdown vault (Obsidian) via GitHub Pages.

Enables AI assistants to search, read, and navigate a folder of Markdown notes as a knowledge base, with full-text search, backlinks, tags, metadata, and note resources for conversation attachment.

Enables querying Markdown files in knowledge bases (like Obsidian vaults) by front matter metadata, filtering notes by tags, recency, and folders to surface relevant content based on created/updated dates.

Headless semantic MCP server for Obsidian, Logseq, Dendron, Foam, and any markdown folder. Features built-in hybrid semantic search, surgical AST editing, template scaffolding, zero-config local embeddings, and workflow tracking.

Python MCP server for programmatic access to markdown-based knowledge vaults, enabling AI assistants to browse, read, search, update, and manage notes, tasks, and projects.

Enables AI assistants to query and interact with a graph database of markdown notes, extracting entities like wikilinks, mentions, and hashtags.

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.

一个基于FastMCP协议的文件向量化服务，支持自动目录监控、多格式文件处理和高效向量搜索，帮助构建个人或团队知识库。

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.

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

Enables AI agents to maintain a structured Markdown or Obsidian memory vault with tools for reading, writing, searching, and organizing notes.

Enables AI agents to persist all generated deliverables as structured Markdown in a project's /.aiartefact directory, with automatic frontmatter, metadata indexing, and full-text search.

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.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Local-first memory server for AI coding agents that stores work sessions, tasks, and durable memories in Markdown files, exposed through MCP tools for session management and memory retrieval.

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

Provides hybrid search over local markdown knowledge bases using BM25 keyword search and vector semantic search. Enables indexing, querying, and retrieving markdown documentation with dual-mode support for local and remote agents.

Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.