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MCP Notes Connector MCP Server

Evernotes MCP Server

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "anotherstream-mcp-notes-connector": {
      "command": "python",
      "args": [
        "-m",
        "mcp_notes_connector.server"
      ],
      "env": {
        "EVERNOTE_TOKEN": "YOUR_EVERNOTE_DEVELOPER_TOKEN",
        "EVERNOTE_SANDBOX": "true"
      }
    }
  }
}

You can run this MCP server to access Evernote resources through the Model Context Protocol. It exposes notes, notebooks, and tags via the Evernote API, enabling you to build MCP clients that read and manipulate Evernote data in a standardized way.

How to use

Start the MCP server locally and connect your MCP client to it. You can run it using the Python module entry point or the standalone CLI after installation. Once running, your MCP client can request access to Evernote resources such as notes, notebooks, and tags through the standard MCP interfaces.

How to install

Prerequisites: you need Python and a virtual environment to isolate dependencies.

# Prerequisites: ensure Python is installed
python --version

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install runtime dependencies
pip install -r requirements.txt

# Optional: install development version
pip install -e ".[dev]"

Configure environment variables for the Evernote integration. Create a .env file and set the following values.

EVERNOTE_TOKEN=your_evernote_developer_token
EVERNOTE_SANDBOX=true  # true to use the sandbox environment

Run the MCP server either as a Python module or via the CLI after installation.

# Option 1: run via Python module
python -m mcp_notes_connector.server

# Option 2: run via CLI if installed
mcp-notes-connector

Configuration and environment

The server relies on environment variables to access Evernote resources. Provide your Evernote developer token and indicate whether to use the sandbox environment. You can place these in a .env file or export them in your shell session.

Development notes

If you are developing or testing, you can run tests and format code as part of your workflow.

pytest

# Code format and lint
black src/ tests/
ruff check src/ tests/

# Type checking
mypy src/