
WorkFlowy MCP Server
Enables interaction with WorkFlowy's outline and task management system through 8 comprehensive tools. Supports creating, updating, searching, and managing hierarchical nodes and tasks with high-performance async operations.
README
WorkFlowy MCP Server
A Model Context Protocol (MCP) server that integrates WorkFlowy's outline and task management capabilities with LLM applications like Claude Desktop.
Features
- 8 MCP Tools for complete WorkFlowy node management
- FastMCP Framework for reliable MCP implementation
- High Performance with async operations and rate limiting
- Automatic Retry with exponential backoff
- Structured Logging for debugging and monitoring
MCP Tools Available
Tool | Description |
---|---|
workflowy_create_node |
Create new nodes with name, notes, and priority |
workflowy_update_node |
Update existing node properties |
workflowy_get_node |
Retrieve a specific node by ID |
workflowy_list_nodes |
List nodes with filtering and pagination |
workflowy_delete_node |
Delete a node and its children |
workflowy_complete_node |
Mark a node as completed |
workflowy_uncomplete_node |
Mark a node as uncompleted |
workflowy_search_nodes |
Search nodes by text query |
Quick Start
Prerequisites
- Python 3.10 or higher
- WorkFlowy account with API access
- Claude Desktop or other MCP-compatible client
Installation
Option 1: Install from PyPI (Recommended)
# Install the package
pip install workflowy-mcp
Option 2: Quick Setup Script
# Download and run the setup script
curl -sSL https://raw.githubusercontent.com/yourusername/workflowy-mcp/main/install.sh | bash
# Or on Windows:
# irm https://raw.githubusercontent.com/yourusername/workflowy-mcp/main/install.ps1 | iex
Option 3: Manual Installation from Source
# Clone the repository (if you want to contribute or modify)
git clone https://github.com/vladzima/workflowy-mcp.git
cd workflowy-mcp
pip install -e .
Configuration
-
Get your WorkFlowy API key:
- From WorkFlowy
-
Configure Claude Desktop or another client: Edit your client configuration (Claude Desktop example):
- Mac:
~/Library/Application Support/Claude/claude_desktop_config.json
- Windows:
%APPDATA%\Claude\claude_desktop_config.json
Add to the
mcpServers
section:{ "mcpServers": { "workflowy": { "command": "python3", "args": ["-m", "workflowy_mcp"], "env": { "WORKFLOWY_API_KEY": "your_actual_api_key_here", // Optional settings (uncomment to override defaults): // "WORKFLOWY_API_BASE_URL": "https://beta.workflowy.com/api", // "WORKFLOWY_REQUEST_TIMEOUT": "30", // "WORKFLOWY_MAX_RETRIES": "3", // "WORKFLOWY_RATE_LIMIT_REQUESTS": "60", // "WORKFLOWY_RATE_LIMIT_WINDOW": "60" } } } }
- Mac:
-
Restart your client to load the MCP server
Usage
Once configured, you can use WorkFlowy tools with your agent:
"Create a new WorkFlowy node called 'Project Ideas' with high priority"
"List all my uncompleted tasks"
"Search for nodes containing 'meeting'"
"Mark the node with ID abc123 as completed"
"Update the 'Weekly Goals' node with new notes"
Development
Setup Development Environment
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install in development mode
pip install -e ".[dev]"
# Run tests
pytest
# Run with coverage
pytest --cov=workflowy_mcp
# Run linting
ruff check src/
mypy src/
black src/ --check
Project Structure
workflowy-mcp/
├── src/
│ └── workflowy_mcp/
│ ├── __init__.py
│ ├── __main__.py # Entry point
│ ├── server.py # FastMCP server & tools
│ ├── config.py # Configuration
│ ├── transport.py # STDIO transport
│ ├── client/
│ │ ├── api_client.py # WorkFlowy API client
│ │ ├── rate_limit.py # Rate limiting
│ │ └── retry.py # Retry logic
│ ├── models/
│ │ ├── node.py # Node models
│ │ ├── requests.py # Request models
│ │ ├── config.py # Config models
│ │ └── errors.py # Error models
│ └── middleware/
│ ├── errors.py # Error handling
│ └── logging.py # Request logging
├── tests/
│ ├── contract/ # Contract tests
│ ├── integration/ # Integration tests
│ ├── unit/ # Unit tests
│ └── performance/ # Performance tests
├── pyproject.toml # Project configuration
├── README.md # This file
├── CONTRIBUTING.md # Contribution guide
├── install.sh # Unix/Mac installer
└── install.ps1 # Windows installer
Running Tests
# Run all tests
pytest
# Run specific test categories
pytest tests/unit/
pytest tests/contract/
pytest tests/integration/
pytest tests/performance/
# Run with coverage report
pytest --cov=workflowy_mcp --cov-report=html
# Run with verbose output
pytest -xvs
API Reference
Node Structure
{
"id": "unique-node-id",
"nm": "Node name",
"no": "Node notes/description",
"cp": false, # Completed status
"priority": 2, # 0-3 (0=none, 1=low, 2=normal, 3=high)
"ch": [], # Child nodes
"created": 1234567890, # Unix timestamp
"modified": 1234567890 # Unix timestamp
}
Error Handling
All tools return a consistent error format:
{
"success": false,
"error": "error_type",
"message": "Human-readable error message",
"context": {...} // Additional error context
}
Performance
- Automatic rate limiting prevents API throttling
- Token bucket algorithm for smooth request distribution
- Adaptive rate limiting based on API responses
- Connection pooling for efficient HTTP requests
Contributing
See CONTRIBUTING.md for development setup and contribution guidelines.
License
MIT License - see LICENSE file for details.
Support
Acknowledgments
- Built with FastMCP framework
- Integrates with WorkFlowy API
- Compatible with Claude Desktop and other MCP clients
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.
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.
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.

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.