Mem AI (Knowledge Workspace) MCP. Find context by meaning, not keywords.

Listen up, you're gonna connect your whole damn knowledge base—everything you write down, every meeting note, every random thought—directly to your AI agent. This isn't just a search bar; it’s an engine that lets your agent talk directly to your memories so you don't have to click through some bloated dashboard.

It handles all the heavy lifting of turning messy notes into structured data.

When you need info, you don't search by keywords; you search by meaning. You run a semantic search using search_mems across every single indexed piece of knowledge in your workspace to pull out contextually relevant answers. If you're digging for all the notes related to 'Project Phoenix' from Q2, the system doesn't just spit back documents containing those words; it figures out what you mean and pulls that specific context.

Need to write something new? You got two ways: first, use mem_it for a quick capture. This is your 'thought dump' shortcut; you log an immediate idea, link, or snippet without having to navigate anywhere. If the thought needs more structure, you can run create_mem. This tool lets you generate and save a brand-new note using full Markdown formatting directly into your knowledge base.

To keep things from becoming a giant digital dumpster fire, you gotta organize this stuff. You first list all available groupings with list_collections, giving you the names and IDs of every thematic collection you've set up. If you need to start a new category, use create_collection to establish a brand-new logical container for related notes.

Once that container exists, you can attach existing notes into it using add_mem_to_collection. This keeps your project data neat and manageable.

When you want to know what's in those collections, you run list_collection_mems against a specific collection ID, which lists every note body inside. If you just need the metadata details about a container without seeing its contents, use get_collection. To get the full context and metadata for one single note by its unique identifier, you call get_mem.

Need to see everything you've ever written? You can list all notes across the entire global knowledge base using list_mems, but be warned—that payload is massive.

Keeping things up-to-date and clean is just as important. If a note needs fixing, you use update_mem to replace the full text content of an existing mem in place; it swaps out old strings for new ones without losing any context mapping or history. If a piece of data is trash and you never want to see it again, you permanently delete it using delete_mem.

This action is irreversible, so double-check what you're tossing.

This whole setup lets your agent manage structured collections by giving you the option to retrieve just the metadata details via get_collection, or list every note body inside that container with list_collection_mems. The system keeps all these moving parts—the capture, the structure, and the deep search—running so your AI client always has a precise, organized view of your personal knowledge.