How to Use Cursor with LearnDash

Overview

Cursor is an AI-powered coding environment that also works as a powerful interface for managing LearnDash with the Model Context Protocol (MCP). Unlike Angie, which has a hosted MCP server built-in, Cursor requires you to set up an external MCP server on your local machine.

This means Cursor is ideal for:

• Users who want to connect to advanced LLMs (Claude, GPT-4, Mistral, etc.)
  
• Organizations that require privacy and local control
  
• Developers building custom LearnDash workflows
  

Note: Connecting Cursor to LearnDash requires creating a WordPress application password. This allows the MCP server to authenticate securely with your site.

Before You Begin

Make sure you have the following:

• A LearnDash site with administrator access
  
• WordPress 5.6 or higher (for application passwords)
  
• A WordPress application password generated for MCP (see [Creating WordPress Application Passwords for MCP])
  
• Cursor installed on your machine
  
• A backup of your WordPress site
  

Always back up your site before using AI agents to make changes. LLMs execute actions step by step, but vague prompts or errors can lead to unintended changes.

Step 1: Install Cursor

1. Download the latest version from cursor.so.
   
2. Install it following the instructions for your operating system.
   
3. Open Cursor and log in or create an account.
   

Cursor looks like a coding editor, but you can interact with it using natural language prompts.

Step 2: Set Up a Local MCP Server

Use Cursor’s guided prompts to create a local MCP server for LearnDash.

Prompt Example:

“I want to set up a local MCP server for my LearnDash site at [your-site-url]. Help me install dependencies, generate an application password, and connect to the LearnDash REST API.”

Cursor will guide you through:

• Installing the necessary packages
  
• Creating a local MCP server
  
• Using your WordPress application password to authenticate
  
• Confirming the connection
  

Step 3: Test the Connection

Once setup is complete, test the MCP server connection:

Prompt Example:

“Check if the LearnDash MCP server is connected. Retrieve my list of active courses.”

If the server responds with a course list, you’re ready to proceed.

Step 4: Start Prompting

Cursor can now manage LearnDash using natural language prompts. For best results, keep prompts clear and limited to 3–5 actions.

Examples:

Create Courses

“Create three courses titled ‘Physics 1,’ ‘Physics 2,’ and ‘Physics 3.’ Set their start dates to February 1, 2026, and their end dates to May 30, 2026. Set access mode to closed.”

Add a Lesson

“In the course ‘Intro to Botany,’ create a lesson called ‘Photosynthesis Basics’ and save it as a draft.”

Enroll Students from a CSV

“Parse this CSV file and enroll all users into the course ‘Safety Training 2025.’ Use the ‘email’ column to match existing WordPress users.”

Prompting Best Practices

Cursor supports advanced models, which makes it powerful—but prompts must be carefully written.

Guidelines:

• Limit each prompt to 3–5 operations
  
• Use exact course names and field values
  
• Break complex tasks into smaller steps
  
• Always specify draft vs. published when creating content
  
• Use precise dates instead of relative terms like “next semester”
  

For tested prompt templates, see the LearnDash AI Prompt Library.

Troubleshooting Cursor MCP Setup

If you run into errors:

• 401/403 errors → Regenerate your application password and confirm your username.
  
• “Cannot connect” → Restart the MCP server and check your site URL.
  
• Prompt runs but nothing changes → Rephrase the prompt with exact field names.
  
• Endpoint not found → Some requests may require the Learndash-Experimental-Rest-Api header.
  

For more, see Troubleshooting Common MCP Errors.

Privacy and Security

With Cursor:

• The MCP server runs locally on your machine.
  
• LearnDash never sees your prompts, responses, or data.
  
• Privacy depends on the LLM you connect (e.g., OpenAI, Anthropic, Ollama).
  

For the highest privacy, pair Cursor with a local open-source model such as Ollama.

Summary

Cursor provides advanced flexibility for LearnDash users who want to run their own MCP server and connect to powerful AI models. With proper setup and structured prompts, Cursor can automate grading, manage enrollments from CSV files, and build courses from existing materials—all while giving you complete control over privacy and data flow.