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2025-04-07

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.

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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

  1. First, make sure the MCP CLI tools are installed:
uv pip install mcp[cli]
  1. Start the Memory MCP server in one terminal:
memory-mcp
  1. 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:

  1. Check the server logs in the terminal where your server is running for any error messages.
  2. In the MCP inspect terminal, enable debug mode with debug on to see raw requests and responses.
  3. Ensure the tool parameters match the expected schema (check with the tool command).
  4. 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

    5 (1)
    Avatar
    user_UQaQhLgJ
    2025-04-17

    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!