mcp-server-demo (AI sticky notes)

AI Sticky Notes: A Model Context Protocol (MCP) application that enables Claude Desktop to create, read, and manage persistent sticky notes.

Overview

AI Sticky Notes leverages the Model Context Protocol to provide a seamless integration between Claude Desktop and a simple note-taking system. This implementation allows Claude to:

• Save notes to persistent storage
• Retrieve all saved notes
• Access the most recently added note
• Generate prompts to summarize existing notes

Installation

Prerequisites

• Python 3.7+
• MCP Python SDK
• Claude Desktop application

Setup

1. Clone this repository:

2. Install `uv`

3. Create a virtual environment

4. Activate the virtual environment

5. Install dependencies from `uv.lock`

6. Run the MCP server

If the configuration is not reflected in Claude Desktop (Menu Bar -> Settings -> Developer), Try to force shutdown Claude Desktop from task Manager et reopen it. When working, you should see "AI Sticky Notes MCP Server" running.

Core Components

from mcp.server.fastmcp import FastMCP
mcp = FastMCP("AI Sticky Notes")

The server registers itself as "AI Sticky Notes" in the MCP ecosystem, making it discoverable by clients like Claude Desktop.

Notes are stored in a simple text file (notes.txt), with each note on a separate line. The file is automatically created if it doesn't exist.

The server exposes the following MCP capabilities:

Using with Claude Desktop

Once the server is running, Claude Desktop will automatically discover and connect to it. You can interact with your notes through natural language:

You: "Please save this as a note: Remember to schedule a team meeting for Friday"
Claude: [Uses add_note tool] "Note saved!"

You: "What notes do I have?"
Claude: [Uses read_notes tool] "Here are your notes: ..."

You: "What was my most recent note?"
Claude: [Accesses notes://latest resource] "Your most recent note is: ..."

You: "Can you summarize my notes?"
Claude: [Uses note_summary_prompt] "Here's a summary of your notes: ..."

Resources

• Model Context Protocol Documentation: https://modelcontextprotocol.io/introduction

• FastMCP API Reference: https://github.com/jlowin/fastmcp

• Claude Desktop Documentation: https://docs.anthropic.com/en/docs/welcome