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.
MCP Server
@PyBhagya
Basic information
About MCP Server
A custom server project built using the Model Context Protocol (MCP) in Python. This repository documents my learning, experiments, and development progress.
MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"model-context-protocol-pybhagya": {
"command": "python",
"args": [
"main.py"
]
}
}
}MCP Server 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 model-context-protocol?
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.
How to use model-context-protocol?
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.
Key features of model-context-protocol
- Persistent note storage using a file‑based backend
- Add and retrieve notes via MCP tool endpoints
- Resource endpoint for the most recent note
- Prompt template for AI summarization of all notes
- Built on the FastMCP server framework in Python
Use cases of model-context-protocol
- Learning how to structure an MCP server with tools, resources, and prompts
- Building a simple note‑taking API that can be consumed by AI models
- Prototyping a backend for an AI‑assisted sticky notes application
- Extending the server with new functionality like editing or deletion
Frequently asked questions
How do I run model-context-protocol?
Run `python main.py` in the project directory. The server will start using the FastMCP framework.
What dependencies are required?
The README only specifies Python and the MCP framework (FastMCP). Other dependencies are not listed.
What transport protocol does the server use?
The README does not specify the transport (e.g., stdio or HTTP). The default FastMCP transport is likely used but not documented here.
Can I use model-context-protocol with any LLM?
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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