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MCP-Server: AI Sticky Notes

@HKanoje

Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

HKanoje

About MCP-Server: AI Sticky Notes

No overview available yet

MCP-Server: AI Sticky Notes config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

MCP-Server: AI Sticky Notes tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is 🗒️ MCP-Server: AI Sticky Notes?

A lightweight Python server that lets you create, read, and summarize sticky notes using AI tools. It is built on FastMCP and stores notes in a plain text file, making it easy to integrate with MCP-compatible AI clients.

How to use 🗒️ MCP-Server: AI Sticky Notes?

Ensure you have Python 3.7+ and the fastmcp package installed. Run main.py as the entry point. The server exposes tools (add_note, read_notes, get_latest_note, note_summary_prompt) that your AI client can call.

Key features of 🗒️ MCP-Server: AI Sticky Notes

  • Add new sticky notes via add_note(message)
  • View all stored notes with read_notes()
  • Retrieve the most recent note using get_latest_note()
  • Generate an AI summarization prompt via note_summary_prompt()

Use cases of 🗒️ MCP-Server: AI Sticky Notes

  • Quickly jot down reminders or ideas from an AI conversation
  • Retrieve and review all notes during a session
  • Have an AI assistant summarize a collection of notes
  • Serve as a lightweight note‑keeping backend for MCP‑powered agents

Frequently asked questions

1

What tools does the server provide?

It provides four tools: `add_note` (add a new note), `read_notes` (return all notes), `get_latest_note` (fetch the most recent note), and `note_summary_prompt` (generate a summarization prompt).

2

Where are notes stored?

All notes are stored in a file named `notes.txt` in the same directory as the server.

3

What are the runtime requirements?

Python 3.7 or later and the `fastmcp` Python package.

4

How does the summarization work?

Calling `note_summary_prompt()` returns a prompt string (e.g., “Summarize the current notes: …”) designed to be sent to an AI model, which then produces a concise summary.

5

Who created this server?

It was created by [HKanoje](https://github.com/HKanoje).

Comments

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