AI Sticky Notes — MCP Server

A minimal Model Context Protocol (MCP) server that gives an AI assistant simple persistent "sticky notes" — the ability to save short notes to a local file and read them back later, across conversations.

What it exposes

The server (server.py) is built with fastmcp / mcp and exposes:

Tools

Resources

Prompts

Notes are stored in a plain text file, notes.txt, in the same directory as server.py, with each note on its own line. The file is created automatically on first use.

Related MCP server: approveit MCP Server

Project structure

MCPSerrver/
├── server.py          # The MCP server: tools, resource, and prompt definitions
├── client.py           # A standalone test client that exercises the server over stdio
├── notes.txt            # Generated at runtime — stores the notes
├── pyproject.toml        # Project metadata and dependencies
├── uv.lock                # Locked dependency versions
└── .python-version        # Pinned Python version for the project

Requirements

• Python 3.13+

• Dependencies (see pyproject.toml):

  • fastmcp>=2.14.1

  • mcp[cli]>=2.0.0

Setup

Using uv (recommended, uses the committed lockfile):

git clone git@github.com:pallikop/MCPSerrver.git
cd MCPSerrver
uv sync

Or with pip:

pip install fastmcp mcp[cli]

Running the server

The server communicates over stdio, so it's designed to be launched by an MCP-compatible client (like Claude Desktop, Claude Code, or the included test client) rather than run standalone for interactive use:

python server.py

Testing with the included client

client.py is a small standalone script that connects to server.py over stdio and exercises every tool, resource, and prompt the server exposes:

python client.py

It will:

1. List all available tools, resources, and prompts

2. Call add_note with a test message

3. Call read_notes to confirm it was saved

4. Read the notes://latest resource

5. Fetch the note_summary_prompt prompt

Connecting to an MCP client

To use this server with an MCP-compatible client (e.g. Claude Desktop), point the client's MCP server config at server.py, for example:

{
  "mcpServers": {
    "ai-sticky-notes": {
      "command": "python",
      "args": ["/path/to/MCPSerrver/server.py"]
    }
  }
}

Notes on the current implementation

• Notes are stored unencrypted in a local plain-text file (notes.txt) — there's no authentication or multi-user separation.

• add_note always appends; there's currently no way to delete or edit an individual note through the exposed tools.

• The notes file path is resolved relative to server.py's own location, so it will always read/write in the same directory regardless of where the server is launched from.