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
Basic information
This is a personal project to test Claude AI's ability to self-write an MCP Server code for its own use.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"optimized-memory-mcp-serverv2": {
"command": "uvx",
"args": [
"run",
"python",
"-m",
"src.main"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
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No. The README indicates the project has been archived due to faulty project specifications and AI direction that led to endless looping behavior.

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

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
Groundwork: company memory for your AI tools. Docs public. Source closed. Public proof needs no account.
Memlord is a remote MCP server available at https://app.memlord.com.
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.
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