"A knowledge base for storing and accessing information" matching MCP servers:

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

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

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.

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.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge from sources like Obsidian, code, web clippings, transcripts, bookmarks, terminal sessions, and fixes, using FTS5 and semantic search through MCP and REST interfaces.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge from sources like Obsidian, code, and transcripts via MCP and REST APIs. Provides token-optimized search, three-tier memory, and self-learning capture of sessions and fixes so multiple agents can build on prior context.

Enables AI agents to capture, classify, promote, synthesize, and retrieve persistent knowledge from sources such as Obsidian vaults, code repos, YouTube transcripts, bookmarks, terminal sessions, and bug fixes. Exposes tiered cold/warm/hot memory with token-optimized search, context briefings, and cross-source synthesis so multiple agents share one compounding brain across sessions.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge through MCP and REST APIs, with SQLite FTS5 search, tiered memory, and token-optimized context so multiple agents share and compound context across sessions.

Enables AI agents to share persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources such as Obsidian vaults, code, transcripts, bookmarks, and terminal sessions through MCP and REST APIs.

Enables AI agents to capture, classify, promote, synthesize, and retrieve persistent knowledge from sources like Obsidian vaults, code, transcripts, and session logs, serving it through MCP tools and a REST API. Provides tiered, token-optimized memory so multiple agents share one compounding brain instead of starting from zero each session.

Enables AI agents to persistently capture, classify, synthesize, and retrieve knowledge across sessions through MCP and REST interfaces, using tiered memory and token-optimized search. It also syncs Obsidian vaults and supports self-learning from sessions and fixes.

Gives every AI agent a shared, persistent memory by ingesting Obsidian vaults, code, transcripts, bookmarks and terminal sessions into a three-tier SQLite FTS5 store, then serving token-optimized hybrid retrieval, classification, promotion and cross-source synthesis over MCP and REST. This lets multiple agents read from and write to one compounding brain so each session starts with better context than the last.

Enables semantic search over a personal document corpus through MCP tools, allowing users to retrieve ranked chunks, access full document context, and list sources from their semester notes.

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 access to a notes knowledge base via MCP, providing tools for keyword search, note creation, and retrieval, along with browseable note resources.

Read-only access to a local-first Markdown knowledge base: search, read, browse vaults, and ask AI questions grounded in your notes. Requires a locally running Molio desktop app (daemon on 127.0.0.1:3100).

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.

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 AI assistants to manage a personal markdown-based knowledge base with natural language interactions. Supports creating, searching, updating, and organizing notes across categories like people, recipes, meetings, and procedures.

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.