Udemy MCP. Analyze feedback, messages, and entire course catalogs.
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Udemy MCP Server gives your AI agent access to Udemy's catalog, instructor data, and student feedback loops. You can search courses by keyword, pull specific course details, and deep-dive into user sentiment.
It handles everything from retrieving public course reviews for competitor analysis to listing direct messages or QA interactions for active instructors.
Analyze class quality, track engagement metrics, and manage content performance—all through your agent's natural conversation.
What your AI agents can do
Course reviews
Lists student reviews for a single, public Udemy course ID.
Courses
Gets all core details for one specific Udemy course using its unique ID.
Instructor courses
Lists every course currently taught by the authenticated instructor.
Your agent queries the public catalog to find courses based on keywords, allowing you to map out available training topics.
You retrieve course reviews for a specific class ID or gather aggregated reviews for all content taught by an instructor.
The agent pulls the latest direct messages and Q&A questions associated with your authenticated instructor account.
You get specific details—like prerequisites or duration—for a single course using its unique ID.
The system lists all courses taught by an instructor and compiles all related reviews for comprehensive performance tracking.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Udemy MCP Server: 6 Tools for Course Data Retrieval
These six tools let your agent fetch structured data from Udemy, covering everything from public course details to private instructor communications.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Udemy on Vinkius019d7617course reviews
Lists student reviews for a single, public Udemy course ID.
019ddc1ccourses
Gets all core details for one specific Udemy course using its unique ID.
019d7617instructor courses
Lists every course currently taught by the authenticated instructor.
019d7617instructor messages
Retrieves all direct messages sent to or from the authenticated instructor.
019d7617instructor qa
Lists every question asked in Q&A sections across all courses taught by the authenticated instructor.
019d7617instructor reviews
Gathers all student reviews for every course taught by the authenticated instructor.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Udemy, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Udemy. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Analyzing student feedback shouldn't feel like detective work.
Today, if you want to understand why students are struggling with Module 3, you have to jump through hoops. You check the course page for public reviews—maybe finding a vague comment about difficulty. Then you navigate to the Q&A section, copy a question, and paste it into a spreadsheet just to track frequency. If you need to see direct complaints, you might even have to log in and scroll through your private messages.
With this MCP Server, that whole process collapses. You tell your agent: 'What are students struggling with?' It runs `instructor_qa` across all courses, aggregates the results, and gives you a clean list of recurring topics—all without you ever leaving your terminal.
Udemy MCP Server: Track Instructor Feedback
Before, compiling an instructor's total reputation meant running multiple reports: one for all public reviews (`course_reviews`), another for every message (`instructor_messages`), and a third for Q&A. It was fragmented, time-consuming, and always left you feeling like you missed something.
Now, the agent synthesizes it all. You ask for 'full performance metrics,' and it pulls `instructor_courses` data, combines it with aggregated `instructor_reviews`, and presents a single source of truth. It's that simple.
What you can do with this MCP connector
This server gives your agent direct access to Udemy's core data streams, letting you stop clicking around and start pulling actionable insights. You don't need to navigate the site; your AI client just talks to it.
When you wanna map out what topics are floating around in the catalog, you can use courses to grab all the foundational metadata for a single class using its unique ID. That lets you check prerequisites or figure out the course duration before you get deep into anything else.
For public sentiment analysis, course_reviews lets your agent pull every student review for one specific, publicly listed Udemy course ID. If you're doing competitive research, you run that tool to gauge how users are feeling about a competitor's offering.
When the focus shifts to an instructor's body of work, instructor_courses lists every single class they teach, giving you a complete roster of their content. From there, your agent can deep-dive into performance metrics. You can gather all student reviews for every course that instructor taught using instructor_reviews, creating an aggregated view of their overall teaching success.
This is huge for tracking consistency.
If you wanna monitor engagement and direct communication surrounding the instructor's content, your agent handles it. It pulls every question asked in the Q&A sections across all courses taught by that person via instructor_qa. You can also retrieve a full log of direct messages sent to or from the authenticated instructor using instructor_messages, keeping you updated on private interactions.
Basically, whether you're checking public catalog details with courses and then cross-referencing specific feedback loops with course_reviews, or if you’re tracking an entire professional profile by listing the content with instructor_courses and pulling all related communication—including aggregated reviews (instructor_reviews) and Q&A logs (instructor_qa) alongside direct messages (instructor_messages) —your agent handles it.
You'll get clean, structured data every time.
019d7617-d319-708d-9294-6e76e9bf514e 
How Udemy MCP Works
- 1 You prompt your AI client (e.g., 'Find me the top-rated Python course on Udemy.')
- 2 The agent recognizes the intent and calls the appropriate tool, like
courses, passing necessary parameters (keywords, IDs). - 3 The server executes the call, fetching structured data—whether it's a list of reviews or direct messages—and returns it to your AI client for immediate use.
The bottom line is: instead of writing code to talk to Udemy, you just tell your agent what data you need, and it handles the connection and structure.
Who Is Udemy MCP For?
This is for product managers who are tired of manually compiling feedback reports across multiple tabs. It’s for curriculum designers who need to validate course content against market demand. If you're an education analyst, this tool lets you track specific instructor performance metrics—like message volume or review sentiment—without ever logging into the platform yourself.
Uses courses and course_reviews to benchmark internal training against public market leaders, identifying gaps in current curriculum.
Runs instructor_messages and instructor_qa to gauge student pain points directly from communication logs, informing the next content update cycle.
Combines instructor_courses, instructor_reviews, and courses data to build a holistic view of an instructor's total market presence and perceived value.
What Changes When You Connect
- Get a complete picture of student sentiment. Instead of checking one review page at a time, the
instructor_reviewstool gathers all feedback for every course an instructor teaches. You get the full data set needed for deep trend analysis. - Track communication bottlenecks with precision. The
instructor_messagestool lets your agent pull direct messages from students, helping you quickly identify recurring technical questions or areas of confusion that need a dedicated follow-up lesson. - Map out competitor offerings instantly. Use the
coursestool to fetch metadata on specific public courses by ID. This is faster than manual browsing and gives structured data points for comparison against your own catalog. - Automate content gap analysis. By running
instructor_qa, you don't just see questions; you get a list of all unanswered or recurring topics, allowing you to prioritize new course modules based on actual student needs. - Understand the scope of an instructor’s work. The combination of
instructor_coursesandinstructor_reviewsgives you one command that outlines everything taught—the courses themselves, plus all associated feedback data points.
Real-World Use Cases
Benchmarking a Competitor's Course
A L&D Specialist wants to see how well a competitor’s 'Advanced Python' course is rated. They prompt their agent, which uses courses first to verify the ID, and then runs course_reviews. The agent returns a structured data set of sentiment scores, allowing the specialist to write an immediate gap analysis report.
Identifying Student Confusion Points
A Content Strategist notices students are frequently asking about 'Deployment' but no module covers it. They ask their agent to run instructor_qa on all courses, which aggregates the questions and instantly reveals that deployment is the single most requested topic across the whole portfolio.
Auditing an Instructor’s Reputation
A Product Manager needs a total view of an instructor's performance. The agent runs instructor_reviews (for all courses) and then pairs that with instructor_messages. This single operation provides a comprehensive, quantitative measure of the instructor’s overall student satisfaction.
Crisis Management for Student Support
A team needs to address negative feedback quickly. They use course_reviews on a specific course ID flagged with low scores. The agent pulls the reviews, allowing the support team to categorize complaints (e.g., 'poor video quality', 'outdated code') and route them directly to the right content owner.
The Tradeoffs
Treating all data as one giant search.
Asking your agent, 'Tell me everything about this course.' This vague prompt forces the AI to guess which tool or data set you actually need, leading to an incomplete or useless response.
→
Copy/Pasting IDs manually.
Manually finding a review ID in one tab, then having to switch to another platform and paste it into the next tool call. This is slow and error-prone, wasting critical time.
→
Assuming all data types are equal.
Treating direct messages (instructor_messages) the same way you treat public reviews (course_reviews). You need to know that one is private communication and the other is public feedback—they require different analysis.
→
Trying to summarize everything at once.
Prompting, 'Summarize my courses, messages, reviews, and Q&A.' This overloads the agent. Break it down: first get instructor_courses, then run instructor_reviews on that list.
→
When It Fits, When It Doesn't
Use this MCP Server if your primary task involves synthesizing feedback or tracking performance across multiple Udemy assets (e.g., comparing reviews from the public catalog vs. direct student messages). You need to connect disparate data points—messages, Q&A, and structured reviews—into one actionable insight.
Don't use this if you just need simple keyword searching for general knowledge; a standard search engine is fine. Also, don't use it if your goal is only to update course metadata (that requires write access, which isn't available). You must use the specific tools—for example, always run instructor_reviews when analyzing an instructor’s overall performance, instead of just calling course_reviews on a single class.
Common Questions About Udemy MCP
How do I get reviews for a course using the course_reviews tool? +
You provide the specific public Course ID to the course_reviews tool. This limits your search to just that class's feedback, giving you precise sentiment data points.
Can I check all my messages with instructor_messages? +
Yes. The instructor_messages tool retrieves the direct communication logs for the authenticated user. It’s critical for understanding private student concerns that don't make it into public reviews.
What is the difference between course_reviews and instructor_reviews? +
The distinction is scope. course_reviews looks at one specific class ID. instructor_reviews, however, gathers all student feedback across every single course taught by that authenticated instructor.
How do I find out what courses an instructor teaches? +
Run the instructor_courses tool. This gives you a list of every title and ID associated with the current user, allowing you to target your subsequent data pulls accurately.
How do I use the `courses` tool to get metadata for a specific course ID? +
The courses tool pulls core metadata for any given Course ID. This lets your agent confirm details like the full title, description, and curriculum structure before you even need to analyze student feedback or reviews.
Does the `instructor_qa` tool gather questions from only one course? +
No. The instructor_qa function aggregates Q&A across all courses tied to your authenticated instructor account. It's useful for spotting general knowledge gaps or recurring student confusion across your entire teaching portfolio.
What is the scope of data retrieved by `instructor_messages`? +
The tool strictly accesses direct messages sent to your own authenticated user account. It will not show you other students' or instructors' private chats; the access is limited solely to your inbox.
Are there rate limits when calling multiple tools like `course_reviews` and `instructor_reviews`? +
Yes, Vinkius enforces standard API usage limits. If your agent hits a throttle limit, you'll need to implement an exponential backoff strategy in your workflow code to retry the request.
Do I need a special account to use the Udemy API? +
You need an API Client ID and Secret, which can be generated from your Udemy account settings under API clients.
Can I read messages from my students? +
Yes! The MCP supports the Instructor API allowing you to fetch Direct Messages, QA, and unread replies.
Is it possible to list courses from the public catalog? +
Yes, you can search for public courses and request insights over price, categories, and ratings.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.