Does Q-CLI MCP Memory Server support cloud‑based memory?
Currently it supports only a local SQLite database. Cloud‑based memory is a planned future goal.
@gengisb
Basic information
an MCP server implementing a memory feature for Q CLI
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"QCLI-memories-mcp": {
"command": "python3",
"args": [
"mcp_memory_server.py"
]
}
}
}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.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
It is a contextual memory system designed for CLI tools used by developers and cloud engineers. It persists memory across sessions, helping AI assistants remember user information, preferences, code patterns, and architecture decisions.
Clone the repository, install dependencies with pip install -r requirements.txt, and run python3 mcp_memory_server.py. Then configure your MCP client by adding an entry to mcp.json with command python and args pointing to the path of memories_server.py.
#code, #architecture, #personal, #project)Currently it supports only a local SQLite database. Cloud‑based memory is a planned future goal.
It automatically assigns tags such as `#code`, `#architecture`, `#personal`, and `#project` to categorize stored information.
It identifies users from conversation context and maintains separate memory spaces for each user, with potential for shared project contexts across users.
Python is required. The server is installed by cloning the repository and running `pip install -r requirements.txt`.
Add an entry to your `mcp.json` file with `"command": "python"` and `"args": ["PATH_TO_YOUR_FILE/memories_server.py"]`, replacing the path with the actual location of the file.

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