What are the runtime requirements?
Python 3.13.1 or higher, SQLite 3.x, and the uvx server. Dependencies are listed in `requirements.txt`.
@AgentWong
基本情報
This is a personal project to test Claude AI's ability to self-write an MCP Server code for its own use.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"optimized-memory-mcp-serverv2": {
"command": "uvx",
"args": [
"run",
"python",
"-m",
"src.main"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
Optimized Memory MCP Server v2 is a high-performance Python-based Model Context Protocol (MCP) server implementation optimized for Claude Desktop integration. It provides efficient memory management and robust infrastructure component tracking capabilities, using SQLite for persistent storage with connection pooling.
Clone the repository, set up a Python environment (Python 3.13.1+), install dependencies with pip install -r requirements.txt, configure the database URL via the DATABASE_URL environment variable, run database migrations with alembic upgrade head, then start the server with uvx run python -m src.main. In Claude Desktop, set the MCP server URL to http://localhost:8000 and enable the MCP protocol in settings.
Python 3.13.1 or higher, SQLite 3.x, and the uvx server. Dependencies are listed in `requirements.txt`.
Run `uvx run python -m src.main` after installing dependencies, configuring the database URL, and applying migrations with `alembic upgrade head`.
Set the MCP server URL in Claude Desktop to `http://localhost:8000` and enable the MCP protocol in Claude settings. Verify the connection with `curl http://localhost:8000/health`.
Data is stored persistently in a SQLite database. The database URL is configured via the `DATABASE_URL` environment variable (e.g., `sqlite:///path/to/db.db`). Migrations are managed with Alembic.
No. The README indicates the project has been archived due to faulty project specifications and AI direction that led to endless looping behavior.

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference

One permissioned folder your team and your AI agents both log into.

Local-first MCP server that snapshots your project's working state into one context object. Open-core, privacy-first.

Ask your Takibi Base collections anything; get answers with cited passages and a support score.

Mess in, magic out: pour in your D&D and TTRPG notes and LoreKeeper keeps them as a private store for your world. Ask Claude or ChatGPT about it, or build a sourcebook.
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