About Memory MCP
Memory MCP is an MCP server for saving and retrieving memories with SQLite storage. It suits users of MCP-compatible AI tools who need to manage ideas, prompt templates, and preferences through simple memory operations.
Key features
- 01Store memories with titles and content
- 02Retrieve memories by ID or title
- 03List all stored memories
- 04Update and delete memories
- 05Use SQLite storage
Use cases
- 01Save ideas for later retrieval
- 02Keep prompt templates in memory
- 03Record and update personal preferences
MCP support
Exposes tools to remember, retrieve, list, update, and delete memories for MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMemory 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 contentget_memory: Retrieve a specific memory by ID or titlelist_memories: List all stored memoriesupdate_memory: Update an existing memorydelete_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 onto see raw requests and responses. - Ensure the tool parameters match the expected schema (check with the
toolcommand). - 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
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.