Memory MCP

Memory MCP

A Model Context Protocol server that allows users to store, retrieve, update, and delete memories using SQLite storage.

Category
Visit Server

README

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

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured