Sticky Notes
add sticky notes
README
AI Sticky Notes MCP Server
An MCP server that lets an AI assistant save, read, and summarize personal notes. Notes are stored in a local notes.txt file next to the server.
Features
add_notetool: Append a new note.read_notestool: Read all saved notes.notes://latestresource: Retrieve the most recently added note.note_summary_promptprompt: Create a prompt containing the current notes for summarization.
Requirements
- Python 3.12 or newer
pipor uv
Run Locally
Using uv
uv sync
uv run python main.py
Using pip
Create and activate a virtual environment, then install the dependency:
python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt
python main.py
The server communicates over MCP stdio, so it is normally launched by an MCP-compatible client rather than opened directly in a browser.
Test the Server
With the MCP CLI installed, start the MCP Inspector with:
mcp dev main.py
Use the Inspector to call add_note, read_notes, open the notes://latest resource, and run note_summary_prompt.
You can also verify the Python module and syntax directly:
uv run python -c "import main; print('MCP server import ok')"
uv run python -m py_compile main.py
Deploy with Smithery
The repository includes smithery.yaml, which configures Smithery to start the server with:
startCommand:
type: stdio
command: python
args: ["main.py"]
Push the deployment files to a GitHub repository and deploy that repository through Smithery. The important files are:
main.pyrequirements.txtsmithery.yaml
notes.txt is optional because the server creates it automatically when needed. Include it only if you want to ship the sample notes.
Run with Docker
Build and start the included image:
docker build -t ai-sticky-notes .
docker run --rm -i ai-sticky-notes
Project Structure
main.py MCP server implementation
requirements.txt Python dependencies
smithery.yaml Smithery stdio startup configuration
Dockerfile Container build configuration
notes.txt Local note storage, created automatically if absent
Storage Note
Notes are stored in a plain text file. This is suitable for local use and demonstrations, but hosted containers may not preserve the file across restarts or redeployments. Durable production storage should use an external database or persistent volume.
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