About MCP Server POC
A note-taking MCP server for AI assistants that can add notes, retrieve the latest entry, and provide a prompt for summarizing them. Notes are stored in a local text file in the project directory.
Key features
- 01Add notes to a local file
- 02Read the latest note resource
- 03Generate a note-summary prompt
- 04Provide MCP over stdio
Use cases
- 01Capture ideas through an AI assistant
- 02Review the most recent note
- 03Ask an AI to summarize saved notes
MCP support
It exposes a note-adding tool, the latest-note resource, and a summary prompt to MCP clients over stdio.
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 READMEMCP Note Taker (POC)
A proof-of-concept implementation of a Model Context Protocol (MCP) server for AI assistant note-taking, featuring custom tools and resources for managing notes.
Overview
This project demonstrates how to create and use a Model Context Protocol (MCP) server that provides note-taking capabilities to AI assistants (such as Claude, Cursor, and others supporting MCP). The server includes:
- A tool to add notes
- A resource to fetch the latest note
- A prompt to summarize all notes
All notes are stored in a local notes.txt file in the project directory.
Requirements
- Python 3.11
- Required packages listed in requirements.txt
Installation and Setup
- Clone this repository:
git clone https://github.com/yourusername/mcp-server-poc.git
cd mcp-server-poc
- Create and activate a virtual environment:
# Init uv package manager
uv init
# Create a Python 3.11 virtual environment
uv venv
# Activate on Windows
.venv\Scripts\activate
# Activate on macOS/Linux
source venv/bin/activate
- Install the required packages:
uv pip install -r requirements.txt
- (Optional) Install MCP CLI tools if needed for development or alternative integrations:
uv add "mcp[cli]"
Running the Application
To run the MCP server:
uv run mcp
The server will start and wait for connections using the stdio transport method.
Integrating with Cursor
To use this MCP server with Cursor IDE:
- Create or edit the file
~/.cursor/mcp.json(on Windows:C:\Users\<username>\.cursor\mcp.json) with the following content:
{
"mcpServers": {
"mcp-server": {
"command": "python",
"args": [
"ABSOLUTE/PATH/TO/main.py"
]
}
}
}
-
Replace the path with the absolute path to your
main.pyfile.- On Windows, use double backslashes:
C:\\Users\\username\\path\\to\\main.py - On macOS/Linux, use regular slashes:
/Users/username/path/to/main.py
- On Windows, use double backslashes:
-
Restart Cursor completely (including ending any background processes) to load the MCP server.
Features
Tools
- add_note(note: str): Adds a note to the
notes.txtfile and returns a confirmation message.
Resources
- notes://latest: Returns the latest note from the
notes.txtfile, or a message if there are no notes yet.
Prompts
- note_summary_prompt(): Generates a prompt asking the AI to summarize all current notes in
notes.txt.
Technical Details
- All notes are stored in a plain text file named
notes.txtin the project root. This file is created automatically if it does not exist. - The server uses the Model Context Protocol SDK and the
mcp[cli]dependency.
Windows Binary Mode Fix
If you use stdio transport on Windows, you may need to set binary mode for stdin/stdout. See the MCP documentation for details.
Troubleshooting
- Verify that all required packages are installed (
pip listto check) - Check that the absolute path in the configuration file is correct
- Make sure the MCP server is running with the proper version of Python (3.11)
- If you encounter issues, try running the MCP server directly to see any error output
License
Acknowledgements
- This project uses the Model Context Protocol SDK
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.