About 🗒️ MCP-Server: AI Sticky Notes
MCP-Server: AI Sticky Notes is a lightweight Python server for adding, reading, and summarizing notes stored in a text file. It is suited to AI tools that need a simple note store and a prompt containing the current notes for summarization.
Key features
- 01Add notes to a text file
- 02Read all stored notes
- 03Retrieve the latest note
- 04Create a summary prompt from notes
Use cases
- 01Save a quick reminder for later
- 02Review notes accumulated in the file
- 03Retrieve the most recently added note
- 04Ask an AI model to summarize stored notes
MCP support
Uses FastMCP to expose note-management tools and a summarization prompt to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README🗒️ MCP-Server: AI Sticky Notes
MCP-Server is a lightweight and modular Python server that allows you to create, read, and summarize sticky notes using AI tools. It’s built on top of FastMCP, providing a simple API-style interface for managing notes stored in a text file.
📦 Features
- ✅ Add new sticky notes
- ✅ View all notes
- ✅ Retrieve the latest note
- ✅ Generate AI-based summaries of notes
🧠 How It Works
The server uses FastMCP to expose simple tools and resources:
add_note(message)→ Adds a new note to the file.read_notes()→ Returns all stored notes.get_latest_note()→ Fetches the most recent note.note_summary_prompt()→ Returns a summarization prompt for the notes.
All notes are stored in notes.txt in the same directory.
📁 Project Structure
MCP-Server/
│
├── main.py # Entry point for the MCP server
├── notes.txt # Storage for all sticky notes
├── .gitignore # Git ignored files
└── README.md # Project documentation
📜 Sample Notes File
After interacting with the server, your notes.txt might look like this:
Work meeting today at 10 AM
AI agents use autonomous decision-making to complete tasks without human intervention
Types of AI agents include virtual assistants, recommendation systems, and autonomous vehicles
Agent foundations research focuses on ensuring AI systems make decisions aligned with human values
Multi-agent systems involve multiple AI agents interacting and collaborating to solve complex problems
Reinforcement learning is commonly used to train AI agents through trial and error
AI agents often need to handle uncertainty and incomplete information when making decisions
Tools like LangChain and AutoGPT help developers create and deploy AI agents more easily
Ethical considerations in AI agents include transparency, accountability, and preventing harmful behaviors
Agentic AI systems can use tools and interact with external systems to accomplish goals
Claude Code is an agentic command line tool from Anthropic that lets developers delegate coding tasks to Claude
When you call note_summary_prompt(), it will generate a prompt like:
Summarize the current notes: Work meeting today at 10 AM ...
This lets your AI model produce a concise summary like:
📌 Summary: A calendar reminder and a comprehensive overview of AI agents, including types, development techniques (e.g., reinforcement learning), tools (LangChain, Claude Code), and ethical concerns.
🛠️ Requirements
- Python 3.7+
fastmcp
✨ Author
Created by HKanoje — feel free to reach out for feedback or collaboration!
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.