Hey interesting take, especially the part about diminishing returns in memory systems. I agree with the key point: storage whether it’s .md, a DB, or an MCP like your memory-journal is a passive layer, not persistent awareness. The model doesn’t “hold” context between sessions, it rebuilds it each time from whatever you feed into the context window.
I think the issue of “when lookup is actually useful” is basically a retrieval relevance problem, not memory itself. Keyword triggers, like you mentioned, give a one-dimensional and unstable result. A more practical direction is to tie recall to the task or files the agent is working on right now like architectural decisions linked to specific modules, not a global store so memory only comes up when there’s real semantic overlap with the current change. That reduces the overhead you’re talking about.
This is being discussed in a couple other threads too if you want to dig deeper:
- How are people handling context across different AI coding tools?
- Is anyone else tired of re-explaining who you are every time you switch between GPT, Claude, Cursor, etc.?
I’d be curious to hear how you handle significance and trust scores in memory-journal-mcp. Scoring feels like the place where you can claw back most of that overhead.