About AI Sticky Notes Custom MCP Server
AI Sticky Notes Custom MCP Server is an MCP server in the Commerce category: this repository contains a custom MCP (Model Context Protocol) server built using fastmcp — a lightweight Python library for quickly creating MCP servers.
Install
git clone https://github.com/Subhokkhon/AI-Sticky-Notes-Custom-MCP-serverThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Ask AI
Ask AI about AI Sticky Notes Custom MCP Server
Powered by Claude · Grounded in docs
Security checks
- ✓README presentThe repository ships documentation.
5 checks have not been run yet.
README
🧠 AI Sticky Notes – Custom MCP Server
This repository contains a custom MCP (Model Context Protocol) server built using fastmcp — a lightweight Python library for quickly creating MCP servers.
The server, called “AI Sticky Notes”, allows AI models (like Claude or other MCP-compatible clients**) to:
Add new notes
Read existing notes
Retrieve the latest note
Generate summaries of all notes
All notes are stored persistently in a local file called notes.txt.
🗂️ Repository Structure AI-Sticky-Notes/ │ ├── main.py # Main MCP server logic ├── notes.txt # File where all notes are stored └── README.md # Documentation (this file)
⚙️ Features 📝 1. Add a Note
Adds a new note (string) to notes.txt.
Function: add_note(message: str)
Returns: "Note saved!"
📖 2. Read All Notes
Reads and returns all stored notes.
Function: read_notes()
Returns: A concatenated string of all notes, or "No notes yet." if empty.
📌 3. Get the Latest Note
Retrieves the most recently added note.
Resource URI: notes://latest
Returns: The latest note or "No notes yet." if no notes exist.
🧾 4. Generate a Summary Prompt
Creates a natural language prompt for summarizing all notes using the AI client.
Function: note_summary_prompt()
Returns:
"Summarize the current notes: [content]"
"There are no notes yet." if empty
🧩 How It Works
The server uses the FastMCP framework to define a Model Context Protocol interface.
It exposes tools and resources that can be used by any AI model (like Claude) that supports MCP.
When the model calls a function like add_note or read_notes, FastMCP handles the interaction and updates notes.txt accordingly
If your MCP-compatible client (like Claude Desktop) is set up properly, it will detect the server and display it as “AI Sticky Notes”.
Restart Claude.
You can now use commands like:
“Add a note saying ‘Finish project report by Monday.’”
“Show me all my notes.”
“Summarize my notes.”
“What was the last note I added?”
🧠 Example Interactions with Claude
You: “Add a note saying ‘Schedule meeting with team.’” Claude: “Note saved!”
You: “Show me all notes.” Claude:
- Finish project report by Monday.
- Schedule meeting with team.
You: “Summarize my notes.” Claude: “You have two tasks: finalize a project report and arrange a team meeting.”
🪶 File Details main.py
Implements:
MCP server initialization
Tools (@mcp.tool)
Resource (@mcp.resource)
Prompt (@mcp.prompt)
Ensures that the notes.txt file exists using the helper function ensure_file().
notes.txt
Plain-text file used to store all notes. Each note is stored on a separate line.
🧰 Technologies Used
Python 3.8+
FastMCP — for building the MCP server
Model Context Protocol (MCP) — for communication with AI models
Claude AI — as an example MCP client
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.