"An overview of knowledge graphs and their applications" matching MCP servers:

Lets an AI assistant search, read, and write notes and sources in your self-hosted Open Notebook, with approval-gated writes and no data sent anywhere else. It bundles tools, prompts, and skills for meeting notes, decision records, weekly reviews, and source-cited research.

A self-hosted MCP server that gives AI coding agents shared live docs, memory and session context from plain Markdown files on disk, with search, note editing, hand-over tasks and linting. It also provides a web UI with a graph of how notes connect, password-protected tokens, and offline sync.

Enables agents to search, read, list, and amend a git-backed markdown knowledge base, automatically committing and publishing changes.

Enables AI agents to search, read, create, and maintain a local-first knowledge base using hybrid retrieval and Markdown note management through the Model Context Protocol.

A Cloudflare Workers-based MCP server for Notion knowledge base management, enabling intelligent search, automatic recording of code snippets, and statistics analysis through natural language.

Enables saving URLs, local files, plain text, and platform content (WeChat, X, YouTube, PDF, images, audio/video, Telegram) as structured Markdown knowledge cards in an Obsidian vault, with optional AI-powered classification and tagging.

Enables read-only querying of an Obsidian knowledge base through MCP tools and resources, including full-text search, node and link retrieval, related-note traversal, and pathfinding between concepts.

Enables saving and searching local notes, with tools to list notes and search backup files.

Provides coding agents with a shared, Markdown-based engineering knowledge base to search, capture, create, and update internal conventions, API details, infrastructure configs, and development setup via lightweight MCP tools.

Read-only MCP server that surfaces contradictions in an Obsidian vault's knowledge graph via semantic search and reasoning edges.

A personal knowledge base MCP server that allows AI assistants to manage notes, tasks, and ideas through tools, resources, and prompts.

Enables AI tutors to maintain durable, local-first learning state by modeling goals as prerequisite-aware knowledge trees, reusable concepts, and evidence-gated mastery.

Enables Claude clients to access notes, guidance, projects, and search from a knowledge library, with explicit-save write tools for creating, updating, and appending content only when the user approves.

Enables Claude to save, retrieve, update, search, and organize personal ideas and reusable analytical frameworks in a private SQLite-backed knowledge base. Supports local desktop use or always-on access across a user's own devices over a private network.

Enables Claude Code to interact with Obsidian vaults by providing tools for multi-term searching, reading notes, and exploring tag-based relationships. It allows users to query, analyze, and manage their personal knowledge base directly through natural language.

A local, zero-cost MCP server that fact-checks your Markdown/Obsidian notes using a local LLM (via Ollama) and free sources like Wikipedia and DuckDuckGo, with no cloud API or API keys.

Enables building and managing knowledge workspaces with tools for ingesting content from text, clipboard, or URLs and searching organized knowledge bases. Supports multiple configurable workspaces with separate inbox and knowledge directories for structured information management.

Enables AI agents to share a persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources like Obsidian vaults, code, and transcripts through MCP and REST APIs. Provides token-optimized, tiered retrieval with full-text and semantic search so every agent session builds on accumulated context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent shared knowledge from sources such as Obsidian, code, transcripts, bookmarks, and sessions. It provides token-efficient search, context briefings, and tiered memory through MCP and REST APIs.

Enables AI agents to share a persistent, self-learning memory by ingesting and classifying knowledge from Obsidian, code, transcripts, bookmarks, and terminal sessions, then retrieving token-optimized context through MCP and REST APIs.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge through token-optimized, three-tier retrieval with hybrid keyword and semantic search. Supports bidirectional Obsidian sync, session and bug-fix capture, and a self-learning loop that compounds context across sessions.

Enables AI agents and clients to capture, classify, synthesize, and retrieve persistent knowledge from multiple sources through MCP and REST APIs, with tiered memory and token-optimized context.

Gives AI agents a shared persistent memory by ingesting Obsidian vaults, code, transcripts, web clips, and session or bug-fix captures, then serving token-optimized context briefings, keyword and semantic search, and cross-source synthesis. Enables multiple agents to read and write the same growing knowledge base so each session builds on the last.

An MCP server that utilizes Trello as a knowledge base for storing, searching, and retrieving code snippets and notes. It enables full CRUD operations and organized categorization of information using Trello boards and labels.

Identifies knowledge gaps in markdown vaults, ranks them by priority, generates research questions, and offers long-tail sampling to break confirmation bias.

A lightweight personal knowledge base MCP server that lets clients store, search, organize, and summarize research notes from a local JSON file.

Enables AI agents to share persistent memory by ingesting, classifying, synthesizing, and retrieving knowledge through MCP and REST APIs, with token-optimized, tier-aware search and Obsidian sync.

Enables AI agents to share a persistent, self-improving memory by ingesting sources like Obsidian vaults, code, transcripts, and session captures, then serving them back through search, classification, synthesis, and token-optimized context briefings. It lets multiple agents read and write the same knowledge base, so each session builds on the last without copy-pasting context.

Gives AI agents a shared persistent memory by ingesting notes, code, transcripts, bookmarks, and session captures into a SQLite FTS5 knowledge base they can search, read, classify, promote, and synthesize over MCP. This lets multiple agents retrieve tiered, token-optimized context and compound what they learn across sessions instead of starting from zero.

Enables AI agents to share persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from Obsidian vaults, code, documents, transcripts, bookmarks, and sessions through MCP and REST interfaces with token-optimized search.