The MCP Server Memos allows interaction with a Memos server for memo management through the MCP protocol. You can:
Search memos: Find memos using keywords
Create memos: Add new memos with customizable visibility (PUBLIC, PROTECTED, or PRIVATE)
Retrieve memos: Fetch a specific memo by its ID
List tags: View all existing memo tags with filtering options by visibility and parent
MCP Server Memos 📝
A Python package that provides LLM models with the ability to interact with Memos server through the MCP (Model Context Protocol) interface.
🚀 Features
🔍 Search memos with keywords
✨ Create new memos with customizable visibility
📖 Retrieve memo content by ID
🏷️ List and manage memo tags
🔐 Secure authentication using access tokens
Related MCP server: MCP Python Interpreter
🛠️ Usage
You can include this package in your config file as bellow, just as you use other Python MCP plugins.
📦 Installation
Installing via Smithery
To install mcp-server-memos-py for Claude Desktop automatically via Smithery:
Installing Manually
Command Line
As a Library
🔧 Configuration
Parameter | Description | Default |
| Memos server hostname |
|
| Memos server port |
|
| Access token for authentication |
|
🤝 Available Tools
This MCP server provides the following tools for interacting with Memos:
Tool Name | Description | Parameters |
| List all existing memo tags | -
: The parent who owns the tags (format: memos/{id}, default: "memos/-") -
: Tag visibility (PUBLIC/PROTECTED/PRIVATE, default: PRIVATE) |
| Search for memos using keywords | -
: The keywords to search for in memo content |
| Create a new memo | -
: The content of the memo -
: Memo visibility (PUBLIC/PROTECTED/PRIVATE, default: PRIVATE) |
| Get a specific memo by ID | -
: The name/ID of the memo (format: memos/{id}) |
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
Memos - A lightweight, self-hosted memo hub
MCP (Model Context Protocol) - Protocol for LLM model applications