Workflowy MCP Server Integration

What does “Workflowy” MCP Server do?

The Workflowy MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to interact programmatically with Workflowy, a popular note-taking and project management tool. By providing an MCP-compatible interface, this server allows AI models to connect to Workflowy accounts and perform actions such as searching, creating, updating, and managing nodes (tasks, notes, lists) directly within Workflowy. This integration empowers developers and AI agents to automate workflows, synchronize project milestones, and enhance productivity by seamlessly bridging Workflowy with other AI-powered tools and services. The server uses username and password authentication for access and is designed to be easily integrated into broader AI development environments.

List of Prompts

(No reusable prompt templates were mentioned in the repository. This section is intentionally left empty.)

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List of Resources

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List of Tools

• Search Nodes: Enables searching through Workflowy nodes based on user queries.
• Create Node: Allows creation of new nodes (notes/tasks) in Workflowy.
• Update Node: Permits updating the content or status of existing Workflowy nodes.
• Mark Node as Complete/Incomplete: Lets the user mark nodes as either completed or not completed for efficient task management.

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Use Cases of this MCP Server

• Project Management Automation: AI agents can update project milestones, mark tasks as completed, and suggest new tasks based on Workflowy data.
• Knowledge Retrieval: Enables AI to quickly find and summarize notes related to specific projects or topics.
• Workflow Synchronization: Automates the synchronization of Workflowy lists with other tools or codebases, keeping project status consistent.
• Task Suggestion and Planning: AI can analyze existing milestones and suggest next steps or tasks based on project progress.
• Personalized Reporting: Generates summaries or reports from Workflowy data for meetings or status updates.

How to set it up

Windsurf

1. Ensure you have Node.js v18+ installed and a Workflowy account.
2. Open your Windsurf configuration file.
3. Add the Workflowy MCP Server to your mcpServers with:

   {
     "mcpServers": {
       "workflowy-mcp": {
         "command": "npx",
         "args": ["-y", "mcp-workflowy@latest", "server", "start"],
         "env": {
           "WORKFLOWY_USERNAME": "your_username",
           "WORKFLOWY_PASSWORD": "your_password"
         }
       }
     }
   }
   

4. Save changes and restart Windsurf.
5. Verify the server is running and accessible.

Securing API Keys
Use environment variables for credentials as shown above; never hardcode them in your config.

Claude

1. Install Node.js v18+ and ensure Workflowy credentials.
2. Edit your Claude configuration to include:

   {
     "mcpServers": {
       "workflowy-mcp": {
         "command": "npx",
         "args": ["-y", "mcp-workflowy@latest", "server", "start"],
         "env": {
           "WORKFLOWY_USERNAME": "your_username",
           "WORKFLOWY_PASSWORD": "your_password"
         }
       }
     }
   }
   

3. Save and restart Claude.
4. Confirm the MCP server is registered.

Cursor

1. Prerequisite: Node.js v18+ and Workflowy account.
2. Open Cursor’s configuration file.
3. Add the MCP server as:

   {
     "mcpServers": {
       "workflowy-mcp": {
         "command": "npx",
         "args": ["-y", "mcp-workflowy@latest", "server", "start"],
         "env": {
           "WORKFLOWY_USERNAME": "your_username",
           "WORKFLOWY_PASSWORD": "your_password"
         }
       }
     }
   }
   

4. Save and restart Cursor.
5. Check connection status.

Cline

1. Ensure Node.js v18+ is installed; obtain Workflowy credentials.
2. Open Cline’s MCP configuration.
3. Add Workflowy MCP as follows:

   {
     "mcpServers": {
       "workflowy-mcp": {
         "command": "npx",
         "args": ["-y", "mcp-workflowy@latest", "server", "start"],
         "env": {
           "WORKFLOWY_USERNAME": "your_username",
           "WORKFLOWY_PASSWORD": "your_password"
         }
       }
     }
   }
   

4. Save and restart the service.
5. Validate the MCP endpoint.

Note:
Always use environment variables for sensitive information. Example:

{
  "env": {
    "WORKFLOWY_USERNAME": "${WORKFLOWY_USERNAME}",
    "WORKFLOWY_PASSWORD": "${WORKFLOWY_PASSWORD}"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "workflowy-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “workflowy-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the tables above, Workflowy MCP is a focused server with clear core functionality but lacks prompt and resource primitives. Security best practices are observed, and tool coverage is solid for Workflowy use cases. Its score is moderate due to missing advanced MCP features.

MCP Score