🗒️ MCP-Server: AI Sticky Notes
@HKanoje
About 🗒️ MCP-Server: AI Sticky Notes
No overview available yet
Config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTools
No tools detected
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
FAQ from 🗒️ MCP-Server: AI Sticky Notes
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).
Where are notes stored?
All notes are stored in a file named notes.txt in the same directory as the server.
What are the runtime requirements?
Python 3.7 or later and the fastmcp Python package.
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.
Who created this server?
It was created by HKanoje.
Frequently asked questions
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).
Where are notes stored?
All notes are stored in a file named `notes.txt` in the same directory as the server.
What are the runtime requirements?
Python 3.7 or later and the `fastmcp` Python package.
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.
Who created this server?
It was created by [HKanoje](https://github.com/HKanoje).
Basic information
More Memory & Knowledge MCP servers
OpenLore
aakarimServe your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab
Agimem
Persistent memory for AI agents. Hosted MCP server with capsule-scoped key/value storage, tags, TTL expiration, bulk ops, and RBAC.
Obsidian MCP Server
cyanheadsRead, write, search, and surgically edit Obsidian vault notes, tags, and frontmatter via MCP. STDIO or Streamable HTTP.
Context Portal MCP (ConPort)
GreatScottyMacContext Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.
RAG Documentation MCP Server
hannesrudolphAn MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Comments