Goodreads MCP. Analyze book metadata and user reviews instantly.
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Goodreads MCP connects your AI agent to the world's largest book database for literature research and reading lists. Instantly search books, retrieve detailed metadata, build author bibliographies, and audit community reviews without leaving your client environment.
What your AI agents can do
Get author profile
Retrieves detailed professional information about a specific author.
Get book info
Gets the core metadata, such as title and description, for any book.
Get series metadata
Gathers structural information about an entire series of books.
Find books across Goodreads by title or keyword search.
Retrieve specific metadata, like descriptions and formats, for any given book ID.
Access a full biography and list all published works by an author.
Fetch and summarize user reviews and ratings for specific titles or authors.
List all books belonging to a known literary series, keeping the reading order intact.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Goodreads: 8 Tools for Literary Data
Use these eight specific tools to search the database, gather book details, manage series info, or examine user-generated content.
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 Goodreads on Vinkius019d8442get author profile
Retrieves detailed professional information about a specific author.
019d8442get book info
Gets the core metadata, such as title and description, for any book.
019d8442get series metadata
Gathers structural information about an entire series of books.
019d8442get user public profile
Accesses public details from a Goodreads user's profile page.
019d8442get user reviews
Retrieves specific reviews written by users about books.
019d8442get user shelves list
Lists the collection of books a user has marked as 'read' or 'to read'.
019d8442list author books
Generates a list of all titles written by a specific author.
019d8442search books
Searches the entire database for books based on keywords or criteria.
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 Goodreads, then connect any of our 4,900+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,900+ 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 Goodreads. 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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Collecting literary data used to be a nightmare of tabs and copies.
Think about it: You need to write an essay on the cultural impact of a certain genre. Today, you're clicking through Goodreads, opening one tab for book details, another tab for reviews, and then jumping to a third site just to find the author's biography. You end up copy-pasting everything into a messy spreadsheet, spending hours just organizing data that should be ready in minutes.
With this connector, you simply ask your agent: 'Compile all metadata on [Genre] authors.' The system handles the clicks and the cross-referencing. You get structured, clean data pulled from multiple sources—all delivered to you without you ever touching a complex web interface.
Discovering full author histories with Goodreads.
Before this MCP, getting a complete picture of an author meant cross-referencing their early works against their later hits. You'd find one list from the book page and another, incomplete bibliography somewhere else. It was piecing together a puzzle using unreliable fragments.
Now, you ask your agent for the full profile and list all books by that author in one go. The data is complete, accurate, and ready to use. That's the difference.
What you can do with this MCP connector
You can use this connector to make your AI agent act like a professional librarian. Instead of clicking through confusing web pages or juggling multiple databases, you talk to it naturally. Your agent retrieves specific book details, accesses full author biographies, and compiles user reviews and ratings into plain text summaries.
This means whether you're writing academic research or just planning your next read, the data comes straight from one reliable source. When you connect this through Vinkius, your agent handles all that complex searching for you. It pulls metadata, tracks reading series, and even lists public user bookshelves, giving you a complete picture of literary history at your fingertips.
019d8442-6e5c-71b9-9099-f9d347452dac 
How Goodreads MCP Works
- 1 Subscribe to this MCP and enter your Goodreads API credentials.
- 2 Connect your preferred AI client (like Claude or Cursor) to Vinkius.
- 3 Ask your agent a question, such as 'What are the best books in the Mistborn series?' The agent uses the tools to gather all metadata and present it.
The bottom line is, you treat the entire Goodreads database like an extension of your AI client's memory.
Who Is Goodreads MCP For?
Anyone who spends time researching books or writing about literature. This hits the academic researcher drowning in source material, the content creator needing trending topics, and the developer building a book-focused application.
Needs to gather specific metadata on authors (using get_author_profile) or compile multiple sources of book information for a literature review.
Uses the ability to retrieve user reviews and ratings to gauge public interest and identify trending topics for marketing copy.
Integrates book series data (get_series_metadata) and general metadata into a custom application dashboard.
What Changes When You Connect
- Deep dive into authorship: Use the get_author_profile tool to gather a complete professional bio, so you don't have to search multiple websites for author context.
- Sentiment analysis on demand: Run get_user_reviews to quickly gauge public opinion. This lets you see what readers actually think of a title before committing to content.
- Organize large datasets: Need to know the proper order? The get_series_metadata tool maps out entire book series, keeping your reading logic sound.
- Streamline discovery: Instead of browsing slowly, use search_books to pinpoint titles immediately. It's fast and keeps you focused on the data you need.
- Track collections: You can list user bookshelves using get_user_shelves_list, giving you an overview of reading trends across communities.
Real-World Use Cases
A student researching a literary genre
The student needs to understand the background of a specific author. They ask their agent to pull data using get_author_profile, then list all available books with list_author_books. The agent combines this into a single report showing the author's scope and influence.
A marketer tracking competitor book interest
The marketer wants to know which titles are getting buzz. They use search_books to find the main titles, then run get_user_reviews on those results. This reveals high-sentiment topics that could guide their next campaign.
A reader planning a massive reading binge
The user wants to read a trilogy but doesn't know the correct order. They ask for help with get_series_metadata, and the agent provides the full list in chronological sequence, preventing them from accidentally skipping a volume.
A developer building an educational dashboard
The developer wants to show book data alongside user activity. They pull general metadata with get_book_info and cross-reference it with the public profile details of users who reviewed it, providing a rich context layer.
The Tradeoffs
Assuming simple keyword search is enough
Asking the agent to 'find books about space travel' and getting a list of unrelated titles. The system might miss key metadata because it only searched by keywords.
→ First, use search_books for initial discovery. Then, pass specific book IDs into get_book_info or list_author_books. This structured approach guarantees you get the required technical data points.
Forgetting author context
Seeing a great book title but not knowing if the author wrote other things in the genre, limiting research scope.
→ Always run get_author_profile first. This gives you the necessary background and then use list_author_books to see the full breadth of their career.
Mixing up user data calls
Calling get_user_reviews without specifying which book ID they are talking about, resulting in an ambiguous request.
→ Always structure your prompt to reference a specific title or author. For example, 'Get the reviews for [Book Title]' ensures the agent uses get_user_reviews correctly.
When It Fits, When It Doesn't
Use this MCP if your work involves structured literary data: analyzing book metadata, tracking authorship, compiling user-generated review sentiment, or mapping out reading sequences. It’s perfect for academic tools and content generation platforms that rely on established publishing records.
Don't use it if you need real-time sales figures, inventory levels, or current pricing information; this is a historical data source, not a retail feed. If your core task is just finding the general name of a book without any detail, a standard search engine works fine. But if you need to combine that name with an author's bio, genre list, and user critique, this MCP is non-negotiable.
Common Questions About Goodreads MCP
How do I find out what a specific author wrote using list_author_books? +
You provide the author's name or ID first. The tool then returns every title associated with that person, allowing you to build a complete bibliography for your project.
Can I get book details and user reviews using get_book_info and get_user_reviews? +
Yes. You first use the book ID from get_book_info, and then pass that same ID to get_user_reviews to fetch specific community opinions on it.
What is the best way to research a series using get_series_metadata? +
Simply provide the name of the book series. The tool handles grouping all related books and often presents them in recommended reading order, saving you from manual cross-checking.
Does get_user_public_profile show my personal bookshelf contents? +
It shows general public profile data. For a list of books someone has marked as read or want to read, use the more specific get_user_shelves_list tool.
When I use get_author_profile, what credentials do I need to authenticate? +
You must provide your dedicated Goodreads API Key and Secret during setup. The connection uses these credentials for every call to ensure secure access to your data streams.
How can I refine my search results when using the search_books tool? +
The search_books tool supports filtering by specific parameters, such as publication year or genre. You pass these criteria directly into the request payload to narrow down the returned list.
If I use list_author_books, how do I get detailed metadata for every title found? +
You must chain the tools: first run list_author_books to retrieve a batch of book IDs. Then, pass those resulting IDs into the get_book_info tool in a loop to gather full details.
What should I do if my calls to get_user_reviews fail due to rate limits? +
Implement an exponential backoff strategy. If your agent receives a 429 error, pause execution for increasing intervals (e.g., 2s, then 4s, then 8s) before retrying the request.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.