Q-CLI MCP Memory Server
About
an MCP server implementing a memory feature for Q CLI
Details
- Author
- gengisb
- GitHub stars
- 1
- Downloads
- 168
- Categories
- Knowledge Base
Jump to
- Intelligent memory storage with tags (#code, #architecture, #personal, #project)
- Contextual retrieval via exact key, keyword search, or context inference
- Multi‑user support with separate memory spaces per user
- Progressive learning that builds understanding over time
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Q-CLI MCP Memory ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"q-cli mcp memory server": {
"QCLI-memories-mcp": {
"command": "python3",
"args": [
"mcp_memory_server.py"
]
}
}
}
}McpServers
{
"QCLI-memories-mcp": {
"command": "python3",
"args": [
"mcp_memory_server.py"
]
}
}Q-CLI MCP Memory Server
A Contextual Memory System for Developer Tools
MCP Memory Server is a powerful contextual memory system designed specifically for CLI tools supporting developers and cloud engineers. It provides intelligent memory capabilities that persist across sessions, helping AI assistants remember critical information about users, their preferences, code, and architecture decisions.
Overview
This MCP-based memory system allows AI assistants to build a rich understanding of users and their work over time. As users interact with the tool, it intelligently remembers key details from previous sessions, including:
- Personal preferences and information
- Preferred programming languages and frameworks
- AWS infrastructure patterns and configurations
- Code formatting preferences
- Previous errors and their solutions
- Project-specific requirements
The result is a progressively more personalized experience that adapts to individual workflows, eliminating the need to repeatedly explain technical context.
Right now it only supports a local memory DB through SQLlite. the goal is to also allow a cloud-based memory.
Installation
```bash
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