
memory-mcp
An mcp server that you can use to store and retrieve ideas, prompt templates, personal preferences to use with you favourite AI tool that supports the modelcontextprovider protocol.
3 years
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Memory MCP
A Model Context Protocol server for storing and retrieving memories using low-level Server implementation and SQLite storage.
Installation
This project uses uv for dependency management instead of pip. uv is a fast, reliable Python package installer and resolver.
Install using uv:
uv pip install memory-mcp
Or install directly from source:
uv pip install .
For development:
uv pip install -e ".[dev]"
If you don't have uv installed, you can install it following the official instructions.
Usage
Running the server
memory-mcp
This will start the MCP server that allows you to store and retrieve memories.
Available Tools
The Memory MCP provides the following tools:
-
remember
: Store a new memory with a title and content -
get_memory
: Retrieve a specific memory by ID or title -
list_memories
: List all stored memories -
update_memory
: Update an existing memory -
delete_memory
: Delete a memory
Debugging with MCP Inspect
MCP provides a handy command-line tool called mcp inspect
that allows you to debug and interact with your MCP server directly.
Setup
- First, make sure the MCP CLI tools are installed:
uv pip install mcp[cli]
- Start the Memory MCP server in one terminal:
memory-mcp
- In another terminal, connect to the running server using
mcp inspect
:
mcp inspect
Using MCP Inspect
Once connected, you can:
List available tools
> tools
This will display all the tools provided by the Memory MCP server.
Call a tool
To call a tool, use the call
command followed by the tool name and any required arguments:
> call remember title="Meeting Notes" content="Discussed project timeline and milestones."
> call list_memories
> call get_memory memory_id=1
> call update_memory memory_id=1 title="Updated Title" content="Updated content."
> call delete_memory memory_id=1
Debug Mode
You can enable debug mode to see detailed request and response information:
> debug on
This helps you understand exactly what data is being sent to and received from the server.
Exploring Tool Schemas
To view the schema for a specific tool:
> tool remember
This shows the input schema, required parameters, and description for the tool.
Troubleshooting
If you encounter issues:
- Check the server logs in the terminal where your server is running for any error messages.
- In the MCP inspect terminal, enable debug mode with
debug on
to see raw requests and responses. - Ensure the tool parameters match the expected schema (check with the
tool
command). - If the server crashes, check for any uncaught exceptions in the server terminal.
Development
To contribute to the project, install the development dependencies:
uv pip install -e ".[dev]"
Managing Dependencies
This project uses uv.lock
file to lock dependencies. To update dependencies:
uv pip compile pyproject.toml -o uv.lock
Running tests
python -m pytest
Code formatting
black memory_mcp tests
Linting
ruff check memory_mcp tests
Type checking
mypy memory_mcp
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Reviews

user_UQaQhLgJ
As a dedicated user of memory-mcp, I am thoroughly impressed by its performance and reliability. Created by drdee, this tool has significantly enhanced my workflow with its seamless memory management capabilities. The clear documentation on the GitHub page (https://github.com/drdee/memory-mcp) makes it easy to get started and integrate into various projects. Highly recommended for anyone looking to optimize their system's memory usage!