Coursera API & Scraper
The Coursera API returns online-course data as clean JSON.
🤖 Using an AI assistant? Copy this link into ChatGPT / Claude / Cursor — it reads every endpoint and parameter instantly and tells you if this API fits your use case.
The primary search endpoint returns courses and specializations with id, name, URL, type, difficulty, duration, rating, review count, partners and skills, and you can pull a detail, reviews, partners and subjects. It is built for education apps, course-aggregators and learning tools that need Coursera catalog data without a scraper. One ReefAPI key, one shared credit pool, the standard envelope.
Coursera ids, slugs, and the enum casing that differs between input and output
Two things break integrations here. The id you get back is not an id you can send anywhere, and the vocabulary you filter with is not the vocabulary you receive. Everything below was read live on 2026-08-27 from search for "machine learning", detail for slug machine-learning, and detail for the parent specialization.
| Field | Measured value | Note |
|---|---|---|
| id | nOOfCDWeEeuiZgo2K4rorQ | A 22-character opaque id. No action accepts it as input; every action takes slug or url |
| slug | machine-learning | The last path segment of the URL, and the real key. It is not the title: slug machine-learning returns "Supervised Machine Learning: Regression and Classification" |
| type (returned) | COURSE, SPECIALIZATION | Upper snake case |
| type (search filter) | Courses, Specializations, Professional Certificates, Guided Projects, Degrees | Title case and plural. The filter vocabulary and the returned vocabulary do not match |
| difficulty | BEGINNER, INTERMEDIATE, MIXED returned; Beginner, Intermediate, Advanced, Mixed accepted | Same split as type |
| duration | ONE_TO_FOUR_WEEKS, ONE_TO_THREE_MONTHS, THREE_TO_SIX_MONTHS | A bucket, not a number of hours. Returned only, not filterable |
| price | Not returned by any action | Only the booleans is_free and is_coursera_plus tell you anything about cost |
| ratings | average 4.89, rating_count 32,841, comment_count 6,187 | A five-point scale. ratings_by_star is keyed with words: five, four, three, two, one |
| total_enrollments | 1,237,972 on the course, 830,254 on the specialization that contains it | A course and its parent program count separately. Never add them up |
| review author | "RG", "AC", "YD" | Initials only. Coursera publishes no reviewer name, avatar or profile link |
search returns 12 results per page, and on a live call meta.total_count, meta.total_pages and meta.next_page all came back null with has_more false on page 1, yet page 2 returned 12 more results. Do not treat has_more as a stop signal here; page until a page comes back empty, up to the parameter ceiling of 80. Note also that what_you_will_learn and recommended_background arrive with HTML entities left in place, as in "NumPy & scikit-learn", so unescape before you display them.
Real request and response JSON
Captured from the indexed primary action, search, on .
{
"method": "POST",
"url": "https://api.reefapi.com/coursera/v1/search",
"headers": {
"x-api-key": "$REEF_KEY",
"content-type": "application/json"
},
"body": {
"query": "machine learning"
}
}{
"ok": true,
"meta": {
"api": "coursera",
"endpoint": "search",
"mode": "live",
"latency_ms": 1911.4,
"record_count": 12,
"bytes": 789077,
"cache_hit": false,
"stop_reason": "complete",
"page": 1,
"total_count": null,
"total_pages": null,
"has_more": false,
"next_page": null,
"charged_credits": 1,
"version": "1.0.0"
},
"data": {
"results": [
{
"id": "nOOfCDWeEeuiZgo2K4rorQ",
"name": "Machine Learning",
"url": "https://www.coursera.org/specializations/machine-learning-introduction",
"slug": "machine-learning-introduction",
"type": "SPECIALIZATION",
"difficulty": "BEGINNER",
"duration": "ONE_TO_THREE_MONTHS",
"rating": 4.89,
"review_count": 39355,
"partners": [
"DeepLearning.AI",
"Stanford Online"
],
"skills": [
"Unsupervised Learning",
"Supervised Learning",
"Model Training",
"Applied Machine Learning",
"Machine Learning Algorithms",
"Transfer Learning",
"Machine Learning",
"Jupyter",
"Data Ethics",
"Decision Tree Learning",
"Model Evaluation",
"Responsible AI",
"Tensorflow",
"Scikit Learn (Machine Learning Library)",
"NumPy",
"Predictive Modeling",
"Deep Learning",
"Artificial Intelligence",
"Classification Algorithms",
"Reinforcement Learning"
],
"is_free": false,
"is_coursera_plus": false,
"image": "https://d15cw65ipctsrr.cloudfront.net/3a/9d2a7af297483a845340bcfbac6f1e/MLS.course-banners-01_Course-Logo-.png",
"tagline": "#BreakIntoAI with Machine Learning Specialization\nOffered by DeepLearning.AI, and Stanford Online"
},
{
"id": "8UjeMk-mEeit4g4GsxE4dg",
"name": "Machine Learning with Python",
"url": "https://www.coursera.org/learn/machine-learning-with-python",
"slug": "machine-learning-with-python",
"type": "COURSE",
"difficulty": "INTERMEDIATE",
"duration": "ONE_TO_THREE_MONTHS",
"rating": 4.67,
"review_count": 18464,
"partners": [
"IBM"
],
"skills": [
"Unsupervised Learning",
"Supervised Learning",
"Model Evaluation",
"Regression Analysis",
"Scikit Learn (Machine Learning Library)",
"Machine Learning Methods",
"Applied Machine Learning",
"Model Training",
"Predictive Modeling",
"Machine Learning Algorithms",
"Statistical Methods",
"Machine Learning",
"Dimensionality Reduction",
"Python Programming",
"Logistic Regression",
"Model Optimization",
"Classification Algorithms"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://s3.amazonaws.com/coursera-course-photos/3c/77927732934dc0a10a44bd3306833d/1200x1200px_1001823008.jpg",
"tagline": "Offered by IBM"
},
{
"id": "Q0Fc_Yl0EeqdTApgQ4tM7Q",
"name": "IBM Machine Learning",
"url": "https://www.coursera.org/professional-certificates/ibm-machine-learning",
"slug": "ibm-machine-learning",
"type": "PROFESSIONAL_CERTIFICATE",
"difficulty": "INTERMEDIATE",
"duration": "THREE_TO_SIX_MONTHS",
"rating": 4.61,
"review_count": 3711,
"partners": [
"IBM"
],
"skills": [
"Unsupervised Learning",
"Exploratory Data Analysis",
"Autoencoders",
"Feature Engineering",
"Dimensionality Reduction",
"Supervised Learning",
"Generative AI",
"Classification Algorithms",
"Regression Analysis",
"Time Series Analysis and Forecasting",
"Recurrent Neural Networks (RNNs)",
"Convolutional Neural Networks",
"Reinforcement Learning",
"Generative Adversarial Networks (GANs)",
"Generative Model Architectures",
"Scikit Learn (Machine Learning Library)",
"Deep Learning",
"Data Science",
"Machine Learning",
"Python Programming"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://d15cw65ipctsrr.cloudfront.net/78/0b8c921b6346f69278f39ba6ca8128/Professional-Certificate---IBM-Machine-Language.png",
"tagline": "Prepare for a career in machine learning\nOffered by IBM"
},
{
"id": "zh7400XtEeWUww73KBYvPw",
"name": "Machine Learning",
"url": "https://www.coursera.org/specializations/machine-learning",
"slug": "machine-learning",
"type": "SPECIALIZATION",
"difficulty": "INTERMEDIATE",
"duration": "THREE_TO_SIX_MONTHS",
"rating": 4.62,
"review_count": 16274,
"partners": [
"University of Washington"
],
"skills": [
"Model Evaluation",
"Classification Algorithms",
"Regression Analysis",
"Applied Machine Learning",
"Machine Learning Methods",
"Feature Engineering",
"Machine Learning",
"Image Analysis",
"Machine Learning Algorithms",
"AI Personalization",
"Unsupervised Learning",
"Predictive Modeling",
"Classification And Regression Tree (CART)",
"Supervised Learning",
"Bayesian Statistics",
"Statistical Machine Learning",
"Model Training",
"Logistic Regression",
"Statistical Modeling",
"Data Mining"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://d396qusza40orc.cloudfront.net/phoenixassets/machine-learning-s12n/machinelearning.jpg",
"tagline": "Build Intelligent Applications\nOffered by University of Washington"
},
{
"id": "T7hf5jWeEeuGCBL8_hyTUQ",
"name": "Supervised Machine Learning: Regression and Classification",
"url": "https://www.coursera.org/learn/machine-learning",
"slug": "machine-learning",
"type": "COURSE",
"difficulty": "BEGINNER",
"duration": "ONE_TO_FOUR_WEEKS",
"rating": 4.89,
"review_count": 32922,
"partners": [
"DeepLearning.AI",
"Stanford Online"
],
"skills": [
"Supervised Learning",
"Applied Machine Learning",
"Jupyter",
"Scikit Learn (Machine Learning Library)",
"Machine Learning",
"Model Training",
"NumPy",
"Machine Learning Algorithms",
"Predictive Modeling",
"Classification Algorithms",
"Feature Engineering",
"Artificial Intelligence",
"Model Evaluation",
"Data Preprocessing",
"Python Programming",
"Logistic Regression",
"Model Optimization",
"Regression Analysis",
"Algorithms"
],
"is_free": false,
"is_coursera_plus": false,
"image": "https://s3.amazonaws.com/coursera-course-photos/9c/90ae67ecdb4185a4ae79ec9a5ae0b6/Course-Logo--1.png",
"tagline": "Offered by DeepLearning.AI, and Stanford Online"
},
{
"id": "e3wiTwXMEeim8hI7AVpI6A",
"name": "Mathematics for Machine Learning",
"url": "https://www.coursera.org/specializations/mathematics-machine-learning",
"slug": "mathematics-machine-learning",
"type": "SPECIALIZATION",
"difficulty": "BEGINNER",
"duration": "THREE_TO_SIX_MONTHS",
"rating": 4.58,
"review_count": 15098,
"partners": [
"Imperial College London"
],
"skills": [
"Dimensionality Reduction",
"Linear Algebra",
"Regression Analysis",
"NumPy",
"Calculus",
"Unsupervised Learning",
"Applied Mathematics",
"Statistical Methods",
"Descriptive Statistics",
"Model Optimization",
"Mathematical Software",
"Machine Learning Methods",
"Jupyter",
"Statistics",
"Numerical Analysis",
"Applied Machine Learning",
"Geometry",
"Artificial Neural Networks",
"Data Science",
"Data Manipulation"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://d15cw65ipctsrr.cloudfront.net/3e/3974e00aa311e8840ea7bed5c70ad0/Specialization-logo.jpg",
"tagline": "Mathematics for Machine Learning\nOffered by Imperial College London"
},
{
"id": "41SdNshVEeqBKQ6dN5ZVkw",
"name": "IBM Introduction to Machine Learning",
"url": "https://www.coursera.org/specializations/ibm-intro-machine-learning",
"slug": "ibm-intro-machine-learning",
"type": "SPECIALIZATION",
"difficulty": "INTERMEDIATE",
"duration": "THREE_TO_SIX_MONTHS",
"rating": 4.61,
"review_count": 3433,
"partners": [
"IBM"
],
"skills": [
"Unsupervised Learning",
"Exploratory Data Analysis",
"Feature Engineering",
"Dimensionality Reduction",
"Supervised Learning",
"Classification Algorithms",
"Regression Analysis",
"Scikit Learn (Machine Learning Library)",
"Machine Learning Algorithms",
"Statistical Methods",
"Data Preprocessing",
"Applied Machine Learning",
"Model Evaluation",
"Statistical Inference",
"Predictive Modeling",
"Machine Learning Methods",
"Statistical Hypothesis Testing",
"Model Training",
"Data Processing",
"Machine Learning"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://d15cw65ipctsrr.cloudfront.net/8d/bb77a93c14433cb346ef97f5425c0a/ML_Graphic.png",
"tagline": "Learn machine learning through real use cases\nOffered by IBM"
},
{
"id": "-XDgQSfIEe2GwRLfSI7mvQ",
"name": "The Nuts and Bolts of Machine Learning",
"url": "https://www.coursera.org/learn/the-nuts-and-bolts-of-machine-learning",
"slug": "the-nuts-and-bolts-of-machine-learning",
"type": "COURSE",
"difficulty": "ADVANCED",
"duration": "ONE_TO_THREE_MONTHS",
"rating": 4.81,
"review_count": 646,
"partners": [
"Google"
],
"skills": [
"Feature Engineering",
"Decision Tree Learning",
"Applied Machine Learning",
"Supervised Learning",
"Advanced Analytics",
"Statistical Machine Learning",
"Machine Learning",
"Machine Learning Algorithms",
"Unsupervised Learning",
"Analytics",
"Random Forest Algorithm",
"Model Training",
"Model Optimization",
"Predictive Modeling",
"Model Evaluation",
"Python Programming",
"Performance Tuning",
"Classification Algorithms"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://s3.amazonaws.com/coursera-course-photos/68/047951cfef4e138d5e2642e863433b/GCC-Coursera-thumbnail-ADA-nuts-and-bolts-susheela.png",
"tagline": "Offered by Google"
},
{
"id": "_quj9TK6EeyT8xKdcQQUGQ",
"name": "Mathematics for Machine Learning and Data Science",
"url": "https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science",
"slug": "mathematics-for-machine-learning-and-data-science",
"type": "SPECIALIZATION",
"difficulty": "INTERMEDIATE",
"duration": "ONE_TO_THREE_MONTHS",
"rating": 4.63,
"review_count": 3250,
"partners": [
"DeepLearning.AI"
],
"skills": [
"Descriptive Statistics",
"Bayesian Statistics",
"Statistical Hypothesis Testing",
"Probability & Statistics",
"Sampling (Statistics)",
"Statistical Methods",
"Probability Distribution",
"Linear Algebra",
"Statistical Inference",
"Model Optimization",
"Machine Learning Methods",
"Statistics",
"A/B Testing",
"Applied Mathematics",
"Probability",
"Calculus",
"Dimensionality Reduction",
"Applied Machine Learning",
"Data Manipulation",
"Machine Learning"
],
"is_free": false,
"is_coursera_plus": false,
"image": "https://d15cw65ipctsrr.cloudfront.net/8c/b75571d8ad4d24bddff436beab0bad/DL_Square_Banner_Coursera_800x800.png",
"tagline": "Master the Toolkit of AI and Machine Learning\nOffered by DeepLearning.AI"
},
{
"id": "0jJMDlRZEfGm_A4yHBYvkQ",
"name": "Debugging Machine Learning Models with Python",
"url": "https://www.coursera.org/learn/packt-debugging-machine-learning-models-with-python",
"slug": "packt-debugging-machine-learning-models-with-python",
"type": "COURSE",
"difficulty": "INTERMEDIATE",
"duration": "THREE_TO_SIX_MONTHS",
"rating": null,
"review_count": 0,
"partners": [
"Packt"
],
"skills": [
"MLOps (Machine Learning Operations)",
"Model Evaluation",
"PyTorch (Machine Learning Library)",
"Responsible AI",
"Debugging",
"Model Deployment",
"Model Training",
"Model Optimization",
"Deep Learning",
"Test Tools",
"Testability",
"Machine Learning",
"Data Preprocessing",
"Verification And Validation",
"Feature Engineering",
"Python Programming",
"AI Security",
"Test Driven Development (TDD)",
"AI Enablement"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://s3.amazonaws.com/coursera-course-photos/4a/85d505d968455d814f594b4c7b0d21/9781800208582_square.jpg",
"tagline": "Offered by Packt"
},
{
"id": "YEktxVHNEfGVyAr_4g2xqw",
"name": "Introduction to Machine Learning",
"url": "https://www.coursera.org/learn/introduction-to-machine-learning-iml",
"slug": "introduction-to-machine-learning-iml",
"type": "COURSE",
"difficulty": "INTERMEDIATE",
"duration": "ONE_TO_THREE_MONTHS",
"rating": 5,
"review_count": 1,
"partners": [
"Birla Institute of Technology & Science, Pilani"
],
"skills": [
"Supervised Learning",
"Applied Machine Learning",
"Feature Engineering",
"Machine Learning Methods",
"Machine Learning Algorithms",
"Statistical Machine Learning",
"Predictive Modeling",
"Model Training",
"Model Evaluation",
"Scikit Learn (Machine Learning Library)",
"Machine Learning",
"Regression Analysis",
"Bayesian Statistics",
"Artificial Intelligence and Machine Learning (AI/ML)",
"Logistic Regression",
"Classification Algorithms",
"Decision Tree Learning",
"Model Optimization",
"Data Preprocessing",
"Probability & Statistics"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://s3.amazonaws.com/coursera-course-photos/b0/bac2d1463a4ad997298981774398fb/Introduction-to-Machine-Learning.png",
"tagline": "Offered by Birla Institute of Technology & Science, Pilani"
},
{
"id": "RdbleGG8EeyipgpI5l_HwQ",
"name": "Machine Learning Introduction for Everyone",
"url": "https://www.coursera.org/learn/machine-learning-introduction-for-everyone",
"slug": "machine-learning-introduction-for-everyone",
"type": "COURSE",
"difficulty": "BEGINNER",
"duration": "ONE_TO_FOUR_WEEKS",
"rating": 4.54,
"review_count": 316,
"partners": [
"IBM"
],
"skills": [
"Model Evaluation",
"Predictive Modeling",
"Model Training",
"Machine Learning",
"Artificial Intelligence and Machine Learning (AI/ML)",
"Supervised Learning",
"Applied Machine Learning",
"Machine Learning Algorithms",
"Artificial Intelligence",
"Deep Learning",
"Classification Algorithms",
"Unsupervised Learning",
"Regression Analysis",
"Reinforcement Learning"
],
"is_free": false,
"is_coursera_plus": true,
"image": "https://s3.amazonaws.com/coursera-course-photos/c4/b502505a3f495caa0ec3e00d0dc696/Machine-Learning-for-Everyone-image.jpg",
"tagline": "Offered by IBM"
}
]
}
}What the Coursera API does
| Action | Description | Concrete use case | Key params |
|---|---|---|---|
| search | Search Coursera by keyword (any topic, skill, tool or title). Returns a paginated list of result cards — name, url, type (course / specialization / professional certificate / guided project), partner, rating, review count, difficulty, duration, skills and free status — most-relevant first. Optional filters: level, type, language, sort. Feed a result's slug/url into the detail action. Page with `page`; meta.has_more / meta.total_count tell you how many. | Content platforms call search to search Coursera by keyword (any topic, skill, tool or title). | query, level, type, language, sort, ... |
| detail | Full detail for one Coursera course OR program (specialization / professional certificate) by slug or URL. For a course: description, difficulty, estimated workload, rating (average, count, by-star), total enrollments, languages, certificates, what-you'll-learn, recommended background, skills, instructors and partners. For a program: the same plus the list of courses inside it. Pass a search result's url, or a slug with type='specialization' to force the program path. | Research tools call detail to get full detail for one Coursera course OR program (specialization / professional certificate) by…. | slug, url, type |
| reviews | Top public student reviews for a Coursera course or program by slug/url: star rating, review text, author and date. Returns the most-helpful reviews Coursera surfaces on the page (top reviews — Coursera does not expose deep review pagination without login). Use meta.review_count for the total number of ratings. | Community analysts call reviews to get top public student reviews for a Coursera course or program by slug/url. | slug, url, type |
| partners | Browse the universities and companies that partner with Coursera (Stanford, University of Michigan, Google, IBM, …). Returns partner id, name and logo. Use it to map who publishes on Coursera. | Media monitors call partners to get browse the universities and companies that partner with Coursera (Stanford, University of Mic…. | none |
| subjects | Browse Coursera's subject catalogue — the top-level domains (Data Science, Business, Computer Science, Arts & Humanities, …) with their slug and description. Use a subject slug as the search `subject` filter. | Content platforms call subjects to get browse Coursera's subject catalogue. | none |
Call search from your stack
curl -X POST https://api.reefapi.com/coursera/v1/search \
-H "x-api-key: $REEF_KEY" \
-H "content-type: application/json" \
-d '{"query":"machine learning"}'import requests
r = requests.post(
"https://api.reefapi.com/coursera/v1/search",
headers={"x-api-key": REEF_KEY},
json={
"query": "machine learning"
},
)
print(r.json()["data"])const res = await fetch("https://api.reefapi.com/coursera/v1/search", {
method: "POST",
headers: {
"x-api-key": process.env.REEF_KEY,
"content-type": "application/json",
},
body: JSON.stringify({
"query": "machine learning"
}),
});
const { ok, data, meta, error } = await res.json();Ask your MCP-connected assistant: call reefapi.coursera.search with {"query":"machine learning"}.Who uses this API and why
- Course-aggregators call search to list Coursera courses by subject, level and rating.
- Learning apps use detail and reviews to show course info and learner feedback.
- Skills-mapping tools use skills and subjects to match courses to a career path.
Questions developers ask before integrating
Does the Coursera API return course prices?
No. Across search, detail and reviews there is no price, currency or discount field of any kind. What you get is is_free and is_coursera_plus on a search row, which tell you whether the item can be taken for nothing and whether it is included in a Coursera Plus subscription. This differs from the Udemy engine, where detail.price is the list price and the pricing block holds the real one; here there is no number to disagree about because Coursera does not publish one on the page.
Why does slug machine-learning return a course with a different name?
Because slugs are permanent and titles are not. On 2026-08-27, detail for slug machine-learning returned name "Supervised Machine Learning: Regression and Classification", the current occupant of Andrew Ng's original ML course URL. Key your records on slug, keep name as a display field, and expect a name to change under a stable slug.
How do I tell a course from a specialization, and how do I fetch each?
A search row's type comes back as COURSE or SPECIALIZATION and its url shows the same thing: /learn/<slug> for a course, /specializations/<slug> for a program. detail auto-detects when you pass url. If you pass a bare slug it assumes a course, so add type "specialization" for a program. A specialization's detail adds courses[] with the slug of each course inside it plus course_count, which was 3 for machine-learning-introduction.
What does detail return that search does not?
Rather a lot. A live detail read added average_instructor_rating 4.95, content_satisfaction_score 98.0, page_views_last_month 1,863,125, launched_at 2022-06-14T21:27:57.322Z, certificates ["SPECIALIZATION","VERIFIED_CERTIFICATE"], 32 subtitle_languages against a single primary language, the ratings_by_star breakdown, and instructors with courses_taught and learners_reached per person (Andrew Ng: 51 courses, 9,929,504 learners). page_views_last_month in particular is a demand signal you will not find on a search row.
Why do rating_count and comment_count differ so much?
Because most learners rate without writing. The measured course returned rating_count 32,841 against comment_count 6,187, so roughly one rating in five carries text. ratings.average is computed over the ratings, not over the comments, which is why it will not match an average you calculate from the review text you can fetch.
How many reviews can I pull, and who wrote them?
Twenty, and effectively nobody you can identify. A live reviews call returned 20 rows, each with review_id, author, rating and date, and Coursera exposes no deeper pagination without a login. The author field is initials only, "RG" and "AC" in the measured rows. review_id is a composite, 68750271~COURSE!~T7hf5jWeEeuGCBL8_hyTUQ, with the course's opaque id after the second tilde. date is day precision, midnight Z. meta.review_count and meta.rating_average came back null on the reviews call, so take the totals from detail's ratings block.
Why do my filter values not appear in the response?
Because the filters take human-readable values and the response returns Coursera's internal enums. You send level "Beginner" and type "Courses"; you get back difficulty "BEGINNER" and type "COURSE". Both filters are declared on_invalid=ignore, so a wrong value silently widens the search instead of erroring. Map in both directions rather than comparing the strings directly.
Should I add a specialization's enrollments to its courses'?
No, they overlap. detail for the course machine-learning reported total_enrollments 1,237,972 while its parent specialization machine-learning-introduction reported 830,254. Each number counts enrollments in that product, and someone taking the specialization is also enrolled in its three courses. Report them separately or pick one level to aggregate at.
What is the Coursera API?
Coursera API is a ReefAPI endpoint group for coursera It returns live JSON through POST requests under /coursera/v1.
Is the Coursera API free to try?
Yes. ReefAPI starts with 1,000 free credits, no card required. Coursera calls use the same shared credit balance as every other ReefAPI engine.
Do I need a Coursera login or account?
No login to Coursera is needed for the API response. You call ReefAPI with your x-api-key header, and the playground can run live examples before you create a production key.
How fresh is the Coursera data?
The page example is captured from a live search call, and production requests fetch live data through ReefAPI rather than a static sample.
How many credits does the Coursera API use?
Coursera actions currently cost 1 credit per successful call. Failed or blocked calls are free. All APIs draw from one credit pool.
Can I call Coursera from an AI assistant or MCP client?
Yes. Connect ReefAPI once through MCP and your assistant can call coursera actions with the same key, credit pool and JSON envelope used by normal REST requests.