Zettelkasten MCP Server

A Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology, allowing you to create, link, explore and synthesize atomic notes through Claude and other MCP-compatible clients.

What is Zettelkasten?

The Zettelkasten method is a knowledge management system developed by German sociologist Niklas Luhmann, who used it to produce over 70 books and hundreds of articles. It consists of three core principles:

1. Atomicity: Each note contains exactly one idea, making it a discrete unit of knowledge
2. Connectivity: Notes are linked together to create a network of knowledge, with meaningful relationships between ideas
3. Emergence: As the network grows, new patterns and insights emerge that weren't obvious when the individual notes were created

What makes the Zettelkasten approach powerful is how it enables exploration in multiple ways:

• Vertical exploration: dive deeper into specific topics by following connections within a subject area.
• Horizontal exploration: discover unexpected relationships between different fields by traversing links that cross domains.

This structure invites serendipitous discoveries as you follow trails of thought from note to note, all while keeping each piece of information easily accessible through its unique identifier. Luhmann called his system his "second brain" or "communication partner" - this digital implementation aims to provide similar benefits through modern technology.

Features

• Create atomic notes with unique timestamp-based IDs
• Link notes bidirectionally to build a knowledge graph
• Tag notes for categorical organization
• Search notes by content, tags, or links
• Use markdown format for human readability and editing
• Integrate with Claude through MCP for AI-assisted knowledge management
• Dual storage architecture (see below)
• Synchronous operation model for simplified architecture

Examples

• Knowledge creation: A small Zettelkasten knowledge network about the Zettelkasten method itself

Note Types

The Zettelkasten MCP server supports different types of notes:

Type	Handle	Description
Fleeting notes	fleeting	Quick, temporary notes for capturing ideas
Literature notes	literature	Notes from reading material
Permanent notes	permanent	Well-formulated, evergreen notes
Structure notes	structure	Index or outline notes that organize other notes
Hub notes	hub	Entry points to the Zettelkasten on key topics

Link Types

The Zettelkasten MCP server uses a comprehensive semantic linking system that creates meaningful connections between notes. Each link type represents a specific relationship, allowing for a rich, multi-dimensional knowledge graph.

Primary Link Type	Inverse Link Type	Relationship Description
reference	reference	Simple reference to related information (symmetric relationship)
extends	extended_by	One note builds upon or develops concepts from another
refines	refined_by	One note clarifies or improves upon another
contradicts	contradicted_by	One note presents opposing views to another
questions	questioned_by	One note poses questions about another
supports	supported_by	One note provides evidence for another
related	related	Generic relationship (symmetric relationship)

Prompting

To ensure maximum effectiveness, we recommend using a system prompt ("project instructions"), project knowledge, and an appropriate chat prompt when asking the LLM to process information, or explore or synthesize your Zettelkasten notes. The docs directory in this repository contains the necessary files to get you started:

System prompts

Pick one:

• system-prompt.md
• system-prompt-with-protocol.md

Project knowledge

For end users:

• zettelkasten-methodology-technical.md
• link-types-in-zettelkasten-mcp-server.md
• (more info relevant to your project)

Chat Prompts

• chat-prompt-knowledge-creation.md
• chat-prompt-knowledge-creation-batch.md
• chat-prompt-knowledge-exploration.md
• chat-prompt-knowledge-synthesis.md

Project knowledge (dev)

For developers and contributors:

• Example - A simple MCP server.md
• MCP Python SDK-README.md
• llms-full.txt

NB: Optionally include the source code with a tool like repomix.

Storage Architecture

This system uses a dual storage approach:

1. Markdown Files: All notes are stored as human-readable Markdown files with YAML frontmatter for metadata. These files are the source of truth and can be:

   • Edited directly in any text editor
   • Placed under version control (Git, etc.)
   • Backed up using standard file backup procedures
   • Shared or transferred like any other text files

2. Database Index: Functions as an indexing layer that:

   • Facilitates efficient querying and search operations
   • Enables Claude to quickly traverse the knowledge graph
   • Maintains relationship information for faster link traversal
   • Is automatically rebuilt from Markdown files when needed
   • Uses SQLite by default; provide a SQLAlchemy URL via ZETTELKASTEN_DATABASE for PostgreSQL or other backends

If you edit Markdown files directly outside the system, you'll need to run the zk_rebuild_index tool to update the database. The database itself can be deleted at any time - it will be regenerated from your Markdown files.

Installation

Installing via Smithery

To install Zettelkasten MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install zettelkasten-mcp --client claude

Via uvx

uvx --from=git+https://github.com/entanglr/zettelkasten-mcp zettelkasten-mcp --notes-dir ./data/notes --database ./data/db/zettelkasten.db

Via pipx (native macOS/Linux/Windows)

Install from GitHub:

pipx install "git+https://github.com/entanglr/zettelkasten-mcp.git"

If/when published on PyPI, install from PyPI instead:

pipx install zettelkasten-mcp

Run the server:

zettelkasten-mcp --notes-dir ./data/notes --database ./data/db/zettelkasten.db

Native Apple Silicon (non-Docker)

On Apple Silicon Macs, prefer pipx/uvx for native darwin/arm64 execution. Docker images are Linux-only (linux/amd64, linux/arm64) and do not provide a native darwin container target.

Docker image architecture support

The latest image tag is published as a multi-arch image manifest with:

• linux/amd64
• linux/arm64

Backend support in the container image differs by architecture:

Backend	linux/amd64	linux/arm64
SQLite	Supported	Supported
PostgreSQL	Supported	Supported
MySQL/MariaDB	Supported	Supported
SQL Server	Supported	Not supported

linux/arm64 images intentionally skip SQL Server ODBC driver installation (msodbcsql18) because upstream driver packaging is currently amd64-focused in this Docker build strategy.

Local Development

# Clone the repository
git clone https://github.com/entanglr/zettelkasten-mcp.git
cd zettelkasten-mcp

# Create a virtual environment with uv
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
uv add "mcp[cli]"

# Install dev dependencies
uv sync --all-extras

Configuration

Create a .env file in the project root by copying the example:

cp .env.example .env

Then edit the file to configure your connection parameters.

To use PostgreSQL instead of the bundled SQLite database, install the optional driver and point ZETTELKASTEN_DATABASE at a SQLAlchemy URL:

pip install "zettelkasten-mcp[postgresql]"
export ZETTELKASTEN_DATABASE="postgresql+psycopg://user:password@localhost:5432/zettelkasten"

Using pipx:

pipx install "zettelkasten-mcp[postgresql]"
export ZETTELKASTEN_DATABASE="postgresql+psycopg://user:password@localhost:5432/zettelkasten"

ZETTELKASTEN_DATABASE accepts either a filesystem path (default ./data/db/zettelkasten.db) or any SQLAlchemy-compatible URL. Legacy ZETTELKASTEN_DATABASE_PATH / ZETTELKASTEN_DATABASE_URL variables are still honored for backward compatibility.

For MySQL or MariaDB support, install the mysql extra and supply a URL using the pymysql driver:

pip install "zettelkasten-mcp[mysql]"
export ZETTELKASTEN_DATABASE="mysql+pymysql://user:password@localhost:3306/zettelkasten"

Using pipx:

pipx install "zettelkasten-mcp[mysql]"
export ZETTELKASTEN_DATABASE="mysql+pymysql://user:password@localhost:3306/zettelkasten"

For Microsoft SQL Server, install the sqlserver extra and point to an ODBC connection string (requires the appropriate system ODBC driver, e.g. ODBC Driver 18 for SQL Server):

pip install "zettelkasten-mcp[sqlserver]"
export ZETTELKASTEN_DATABASE="mssql+pyodbc://user:password@server/database?driver=ODBC+Driver+18+for+SQL+Server"

Using pipx:

pipx install "zettelkasten-mcp[sqlserver]"
export ZETTELKASTEN_DATABASE="mssql+pyodbc://user:password@server/database?driver=ODBC+Driver+18+for+SQL+Server"

Note for container users: SQL Server support is available in the published Docker image on linux/amd64 only. On linux/arm64, the container does not include SQL Server ODBC drivers, so use SQLite/PostgreSQL/MySQL instead.

Usage

Starting the Server

python -m zettelkasten_mcp

Or with explicit configuration:

python -m zettelkasten_mcp --notes-dir ./data/notes --database ./data/db/zettelkasten.db

Or with PostgreSQL:

python -m zettelkasten_mcp --notes-dir ./data/notes \
  --database postgresql+psycopg://user:password@localhost:5432/zettelkasten

Connecting to Claude Desktop

Using smithery

npx -y @smithery/cli install zettelkasten-mcp --client claude

Manually

Add the following configuration to your Claude Desktop:

{
  "mcpServers": {
    "zettelkasten": {
      "command": "/absolute/path/to/zettelkasten-mcp/.venv/bin/python",
      "args": ["-m", "zettelkasten_mcp"],
      "env": {
        "ZETTELKASTEN_NOTES_DIR": "/absolute/path/to/zettelkasten-mcp/data/notes",
        "ZETTELKASTEN_DATABASE": "postgresql+psycopg://user:password@localhost:5432/zettelkasten",
        "ZETTELKASTEN_LOG_LEVEL": "INFO"
      }
    }
  }
}

Set ZETTELKASTEN_DATABASE to a filesystem path for SQLite or any SQLAlchemy URL (for example PostgreSQL) to choose the backend.

Available MCP Tools

All tools have been prefixed with zk_ for better organization:

Tool	Description
zk_create_note	Create a new note with a title, content, and optional tags
zk_get_note	Retrieve a specific note by ID or title
zk_update_note	Update an existing note's content or metadata
zk_delete_note	Delete a note
zk_create_link	Create links between notes
zk_remove_link	Remove links between notes
zk_search_notes	Search for notes by content, tags, or links
zk_get_linked_notes	Find notes linked to a specific note
zk_get_all_tags	List all tags in the system
zk_find_similar_notes	Find notes similar to a given note
zk_find_central_notes	Find notes with the most connections
zk_find_orphaned_notes	Find notes with no connections
zk_list_notes_by_date	List notes by creation/update date
zk_rebuild_index	Rebuild the database index from Markdown files

Project Structure

zettelkasten-mcp/
├── src/
│   └── zettelkasten_mcp/
│       ├── models/       # Data models
│       ├── storage/      # Storage layer
│       ├── services/     # Business logic
│       └── server/       # MCP server implementation
├── data/
│   ├── notes/            # Note storage (Markdown files)
│   └── db/               # Database for indexing
├── tests/                # Test suite
├── .env.example          # Environment variable template
└── README.md

Tests

Comprehensive test suite for Zettelkasten MCP covering all layers of the application from models to the MCP server implementation.

How to Run the Tests

From the project root directory, run:

Using pytest directly

python -m pytest -v tests/

Using UV

uv run pytest -v tests/

With coverage report

uv run pytest --cov=zettelkasten_mcp --cov-report=term-missing tests/

Running a specific test file

uv run pytest -v tests/test_models.py

Running a specific test class

uv run pytest -v tests/test_models.py::TestNoteModel

Running a specific test function

uv run pytest -v tests/test_models.py::TestNoteModel::test_note_validation

Tests Directory Structure

tests/
├── conftest.py - Common fixtures for all tests
├── test_integration.py - Integration tests for the entire system
├── test_mcp_server.py - Tests for MCP server tools
├── test_models.py - Tests for data models
├── test_note_repository.py - Tests for note repository
├── test_search_service.py - Tests for search service
├── test_semantic_links.py - Tests for semantic linking
└── test_zettel_service.py - Tests for zettel service

Important Notice

⚠️ USE AT YOUR OWN RISK: This software is experimental and provided as-is without warranty of any kind. While efforts have been made to ensure data integrity, it may contain bugs that could potentially lead to data loss or corruption. Always back up your notes regularly and use caution when testing with important information.

Credit Where Credit's Due

This MCP server was crafted with the assistance of Claude, who helped organize the atomic thoughts of this project into a coherent knowledge graph. Much like a good Zettelkasten system, Claude connected the dots between ideas that might otherwise have remained isolated. Unlike Luhmann's paper-based system, however, Claude didn't require 90,000 index cards to be effective.

License

MIT License

Pre-commit (code formatting)

To keep code style consistent, this project uses pre-commit with black and isort configured.

Install and enable the hooks locally:

pip install pre-commit
pre-commit install
pre-commit run --all-files

If you use a virtual environment, make sure the environment is activated before running pre-commit install so the hooks point to the correct Python interpreter.

Editor configuration (VS Code)

This project includes recommended settings for Visual Studio Code to enable automatic import completions and organize imports on save. The workspace settings are in .vscode/settings.json and recommended extensions in .vscode/extensions.json.

Recommended VS Code extensions:

• ms-python.python — Python language support
• ms-python.vscode-pylance — Pylance language server (provides auto-import completions)

Important settings (already configured):

• python.analysis.autoImportCompletions: true — shows auto-import suggestions in completions
• editor.codeActionsOnSave.source.organizeImports: true — runs import sorting on save
• editor.formatOnSave: true with Black as the formatter

Activate the workspace settings by opening the project in VS Code; you may need to install the extensions and reload the window.