You can create a course with Cursor, and the more useful question is whether you should.
All three of these agents can connect to the same MCP server and issue the same instructions. The differences aren’t in capability. They’re in where the agent lives, what it’s optimised for, and whether course work fits the way you already use it.
Here’s each setup, and an honest verdict on which of the three earns a place in a real workflow.
Try Mini Course Generator free – connect whichever agent you already have open.
Key Takeaways
– All three connect to the same endpoint and can do the same things. Pick based on where you already work, not on capability.
– Manus is the most natural fit for non-developers – it’s an agent runtime rather than a code editor, so course work isn’t an odd use of it.
– Cursor earns its place only when the course lives beside code: docs in a repo, training regenerated when the docs change.
– Copilot support depends on your client and version. Verify MCP support in your specific setup before planning around it.
– If you aren’t already using one of these daily, use Claude or ChatGPT instead. A coding agent for course work you don’t otherwise need is friction with extra steps.
They All Connect to the Same Thing
Worth establishing before the setups, because it makes the choice simpler than it looks.
MCP is an open standard. A server exposes a set of actions – create a course, add a quiz, publish, read results – and any client that speaks MCP can call them. The server doesn’t know or care which agent connected.
So the capability question has one answer for all three: yes, they can all build and publish a course. What differs is the ergonomics, and ergonomics is what decides whether you use something twice.
| Manus | Cursor | Copilot | |
|---|---|---|---|
| What it is | An agent runtime | A code editor | A coding assistant in your IDE |
| Where course work feels natural | Yes | Only alongside a repo | Rarely |
| MCP support | Documented | Configured in mcp.json |
Varies by client and version |
| Best for | Non-developers running agent tasks | Docs-in-repo workflows | Teams already standardised on it |
| Setup difficulty | Low | Low | Check first |
Manus
The most natural fit of the three, and the one to try first if you’re not a developer.
Manus is an agent runtime rather than an editor, so pointing it at a task like “read this and build a course” isn’t an unusual use of the product. It’s the intended one.
Setup:
- Open the tool or MCP connection settings.
- Register Mini Course Generator as a new tool source, using
https://mcp.minicoursegenerator.com/mcp. - Grant permission – authorize against your Mini Course Generator account.
- Run the workflow.
Then the prompt is the same as anywhere else:
“Read the onboarding documentation I’ve linked. Build a course in Mini Course Generator with one lesson per process, ordered as a new hire would encounter them, with a quiz after each lesson testing whether they could perform the step. Publish it and give me the link.”
Verdict: use it if it’s already your agent. Manus handles multi-step tasks without you supervising each turn, which suits a job with several stages – read the source, structure it, build, publish, report back.
Cursor
Cursor is a code editor, and that shapes when course creation makes sense in it.
Setup: add the server to your MCP configuration. In Cursor that’s an mcp.json entry:
{
"mcpServers": {
"mcg": {
"url": "https://mcp.minicoursegenerator.com/mcp"
}
}
}
Reload, complete the OAuth authorization when prompted, and the server’s actions become available to the agent.
Verdict: worth it in exactly one situation. If your documentation lives in the repo you’re already working in, Cursor is the best of the three, because the source material and the agent are in the same place:
“Read every file in docs/onboarding/. Build a course in Mini Course Generator with one lesson per file, in the order they’re numbered. Publish it and print the URL.”
Outside that case, opening a code editor to write training is a strange thing to do, and you’ll stop after the second attempt. If docs-in-repo is your setup, the Claude Code piece makes the fuller argument for that workflow, including running it in CI.
Copilot
The one where the honest answer is “check your specific setup first”.
MCP support in Copilot depends on which client you’re using and which version. VS Code’s agent mode has supported MCP servers, and behaviour has changed across releases and differs between the IDE, the CLI and other surfaces.
So the first step isn’t configuration, it’s verification. Confirm that your Copilot client supports remote MCP servers in the version your organisation is running, and check whether adding third-party servers is permitted by your admin policy. In managed enterprise environments, that second question decides it more often than the first.
Verdict: only if you’re already standardised on it. If your organisation runs Copilot everywhere and adding an MCP server is allowed, using it saves introducing another tool. Otherwise the setup friction outweighs anything you gain, because the actions available are identical to the other two.
Which One Should You Actually Use
The short answer for most people is none of them.
These are coding agents. If you’re not writing software, using one for course work means adopting a developer tool to do a non-developer job, and the ergonomics show. Claude and ChatGPT connect to the same server, do the same things, and are built for conversation rather than code.
Use a coding agent when one of these is true:
- You already live in it. The best tool for this is the one already open.
- Your source material is in a repo. Then Cursor or Claude Code is genuinely the right choice, because the docs and the agent share a location.
- You want it in CI. Regenerating training when documentation changes is a real workflow, and it needs an agent that runs headlessly.
If none of those apply, connect a conversational assistant instead and skip this entire article.
What Doesn’t Change Between Them
Whichever you pick, four things are constant, because they’re properties of the connection rather than the client.
The permissions model. OAuth against your account, actions within your own permissions, revocable from wherever you added it.
The output. The same course object, the same published URL, the same analytics.
The review requirement. Content generated from your documents is far more reliable than content generated from general knowledge, and it’s still generated. Somebody who knows the subject reads it before learners do.
The judgement. No agent decides what to teach. A vague brief produces a polished course about nothing, and that’s true in all three.
The Short Version
Cursor, Copilot and Manus all connect to the same MCP server and can all build and publish a course. Capability isn’t the differentiator; ergonomics is.
Manus fits best for non-developers, because it’s an agent runtime and course work is a normal thing to ask it for. Cursor earns its place when your documentation lives in the repo you’re already in, which makes docs-to-training a local operation. Copilot depends on your client version and your admin policy, so verify before planning.
And for most people the right answer is a conversational assistant instead. If you aren’t writing code today, connecting Claude or ChatGPT does the same job without adopting a developer tool to do it.
Frequently Asked Questions
Can Cursor create a course?
Yes, once an MCP server is configured in mcp.json and authorized. It’s most useful when the source documentation lives in the repository you’re already working in, which makes generating training a local operation rather than a context switch.
Does GitHub Copilot support MCP servers?
Support depends on the client and version, and on your organisation’s policy about third-party servers. Verify both before planning a workflow around it – in managed enterprise environments the policy question decides it more often than the technical one.
Is Manus good for course creation?
It’s the best fit of the three for non-developers, because it’s an agent runtime rather than a code editor. Multi-step tasks like read-structure-build-publish are what it’s designed for, so course work isn’t an unusual use of the product.
Which agent produces the best courses?
None of them, individually. They all call the same actions on the same server, so the output is identical. Course quality depends on your source material and your prompt, not on which client sent the request.
Should I use a coding agent for training work?
Only if you already use one daily, your documentation lives in a repo, or you want training regenerated in CI. Otherwise a conversational assistant connects to the same server with far less friction and no developer tooling to maintain.
Can I use more than one agent with the same server?
Yes. The connection is per-client and authorized separately, so you can connect Cursor for repo-based work and Claude for everything else. They act on the same workspace with the same permissions.
