Mnemos MCP server

Mnemos is a memory helper for AI coding assistants. It quietly keeps notes about your project, like why you chose a certain approach or how you fixed a tricky bug, so your assistant does not forget them between sessions. It is handy if you use Claude Code, Cursor, or a similar coding tool and are tired of explaining the same things every morning.

What this MCP server does

You keep coding as usual, and your AI assistant talks to Mnemos behind the scenes. When something worth remembering comes up, like an architecture choice or a bug fix, Mnemos saves it in a small local database. Later, when you ask a question, your assistant searches that database and pulls back the most relevant notes. Mnemos also cleans things up over time, removing duplicates and flagging notes that may be outdated. You just see your assistant giving better answers with less repeating.

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What you can do with it

• Remember architecture decisions and why they were made
• Recall how a bug was fixed last time it appeared
• Store project conventions and coding rules
• Search past notes by keyword or meaning
• Link related memories together, like a fix and its cause
• Clean up old or duplicate notes automatically
• Check what the memory system is doing with a status command

Try asking your AI

• “What did we decide about the JWT token expiry and where is it configured?”
• “Have we seen this database connection error before, and how did we fix it?”
• “What are the naming conventions for our API endpoints in this project?”
• “Summarize the main architecture decisions we made for the auth module.”

What it gives back to you

You get answers in your normal chat with your AI assistant, drawn from your project's saved notes. Sometimes it will be a short summary, sometimes a list of related memories, sometimes a specific fact like a file path or a setting. You will not see raw database output unless you ask for it directly.

Before you start

Who it's for

Developers and coding-agent users who want their AI assistant to remember project details across sessions without repeating themselves.