NoteKeeper MCP
Provides AI assistants with tools to create, list, search, tag, and delete notes stored in a local SQLite database.
README
NoteKeeper MCP
An MCP (Model Context Protocol) server, built with FastMCP, that gives an AI assistant tools to capture, search, tag, and manage notes. Notes are stored in a local SQLite database.
Built to explore the Model Context Protocol: how to expose real, stateful functionality to an LLM through a clean, testable, deployable server.
Features
- Four MCP tools for full note management (create, list, search, delete)
- Persistent storage in SQLite — notes survive across sessions
- Tag support — organize notes with comma-separated tags and search by them
- Safe database access using parameterized queries (SQL-injection safe)
- Fully tested with an isolated pytest suite
- Installable package with a clean
src/layout
Tools
| Tool | Description |
|---|---|
add_note |
Save a new note with optional tags |
list_notes |
List all notes, newest first |
search_notes |
Find notes by keyword in content or tags |
delete_note |
Delete a note by its id |
Installation
Requires Python 3.10+.
# Clone the repository
git clone https://github.com/VRurs1606/notekeeper-mcp.git
cd notekeeper-mcp
# Create and activate a virtual environment
python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS/Linux:
source .venv/bin/activate
# Install the package and its dependencies
pip install -e .
Usage
Run the tests
pytest
Try the demo
A demo client that exercises every tool in-memory:
python demo.py
Connecting to Claude Desktop
To use NoteKeeper with Claude Desktop, add it to your MCP config file:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"notekeeper": {
"command": "python",
"args": ["/absolute/path/to/notekeeper-mcp/src/notekeeper_mcp/server.py"]
}
}
}
Restart Claude Desktop, and you can ask it to save and search notes in natural language.
Project Structure
notekeeper-mcp/ ├── src/notekeeper_mcp/ │ ├── init.py │ ├── server.py # MCP server and tool definitions │ └── database.py # SQLite storage layer ├── tests/ │ ├── conftest.py # Shared test fixtures │ └── test_database.py # Storage layer tests ├── demo.py # In-memory demo client ├── pyproject.toml # Package configuration └── README.md
Tech Stack
- Python 3.10+
- FastMCP — MCP server framework
- SQLite — persistent storage (Python standard library)
- pytest — testing
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.