What does model-context-protocol do?
It is a demonstration MCP server that manages sticky notes (add, read, get latest) and provides a prompt to ask an AI to summarize them.
@PyBhagya
Basic information
A custom server project built using the Model Context Protocol (MCP) in Python. This repository documents my learning, experiments, and development progress.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"model-context-protocol-pybhagya": {
"command": "python",
"args": [
"main.py"
]
}
}
}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.
model-context-protocol is a Python MCP server that implements a simple AI Sticky Notes application. It demonstrates core Model Context Protocol concepts by providing tools, resources, and prompts for note management and AI summarization. It is intended for developers learning MCP or building note‑taking integrations with LLMs.
Run python main.py from the project root to start the server. The server exposes endpoints for adding notes (add_note tool), reading all notes (read_notes tool), accessing the latest note (notes://latest resource), and generating a prompt for note summarization (note_summary_prompt prompt). No additional configuration is required.
It is a demonstration MCP server that manages sticky notes (add, read, get latest) and provides a prompt to ask an AI to summarize them.
Run `python main.py` in the project directory. The server will start using the FastMCP framework.
The README only specifies Python and the MCP framework (FastMCP). Other dependencies are not listed.
The README does not specify the transport (e.g., stdio or HTTP). The default FastMCP transport is likely used but not documented here.
Yes, the server is designed to be connected to LLMs via MCP. The note_summary_prompt generates a prompt that can be sent to any compatible AI.

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