I have also created a MCP memory tool, it has both RAG over past chats and a graph based read/write space. But I tend not to use it much since I feel it dials the LLM into past context to the detriment of fresh ideation. It is just less creative the more context you put in.

Then I also made an anti-memory MCP tool - it implements calling a LLM with a prompt, it has no context except what is precisely disclosed. I found that controlling the amount of information disclosed in a prompt can reactivate the creative side of the model.

For example I would take a project description and remove half the details, let the LLM fill it back in. Do this a number of times, and then analyze the outputs to extract new insights. Creativity has a sweet spot - if you disclose too much the model will just give up creative answers, if you disclose too little it will not be on target. Memory exposure should be like a sexy dress, not too short, not too long.

I kind of like the implementation for chat history search from Claude, it will use this tool when instructed, but normally not use it. This is a good approach. ChatGPT memory is stupid, it will recall things from past chats in an uncontrolled way.