Use SFF Book Awards with an AI Assistant

What You Can Ask

• I liked award-winning space opera. What should I read next?
• Compare three books I name and tell me which is the best entry point.
• What recent fantasy winners have lots of reader engagement?
• Which awards does a book I name have, and what else is similar?
• Explain what the dataset covers before recommending anything.
• Give me overlooked books recognized by awards in the 1990s.
• Show me Hugo-winning novels since 2010 that work as entry points.
• Find fantasy books that were recognized by more than one award family.
• Recommend standalone science fiction with strong reader ratings.
• What are good first books for exploring cyberpunk, high fantasy, or urban fantasy?
• List recent nominees with lower Goodreads rating counts but strong award evidence.
• Compare award recognition and reader engagement for books I am choosing between.

Connection

MCP endpoint

https://book-awards.pages.dev/mcp

Transport

Streamable HTTP

Authentication

None: no OAuth, API key, bearer token, cookies, or user account.

Data source

Generated static public artifacts, not request-time Goodreads or SFADB fetching.

Current Tools

get_dataset_info

Coverage, freshness, facets, scoring, exports, and limits.

search_books

Filtered book retrieval by query, genre, award, subgenre, year, recognition, score, audience size, and series mode.

recommend_books

Goal-oriented recommendations for entry points, recent winners, reader favorites, hidden gems, similar books, and shortlist or subgenre samplers.

get_book

One work by work_key or Goodreads book ID, with optional recognitions, score context, and related books.

get_award

Award-category views with year, recognition, genre, sort, and limit filters.

get_subgenre

Subgenre views with award-year, recognition, series-mode, sort, and limit filters.

compare_books

Compare 2 to 5 works by awards, score, genre, subgenre, series, recency, and audience size.

Agent Contract

• Every tool response includes summary, data, evidence, caveats, freshness, resource_links, follow_up_handles, and suggested follow-ups.
• Use stable handles such as work_key, award keys, subgenre keys, and book-awards:// resource URIs for follow-up calls.
• Cite award recognition, score or rating-count context, and caveats from tool results when making recommendations.
• Do not infer live Goodreads or SFADB state from this endpoint. Request-time external source fetching is disabled.

Resources And Prompts

Resources expose dataset metadata, facets, scoring, exports, works, awards, and subgenres. Award and subgenre resources are bounded summaries with tool hints; use tools for filtered retrieval. Prompt templates cover next-read recommendations, book comparison, and dataset explanation.

Limits And Freshness

• Use get_dataset_info for current facets, coverage, scoring notes, export URLs, and limits before broad recommendations.
• Search and recommendation calls are bounded for agent use. Book-related recommendations are capped at 10.
• Goodreads ratings are public edition metadata and may be missing for some works.
• Subgenres are controlled local taxonomy assignments, not raw Goodreads shelves.
• Machine-readable dataset metadata is also available at https://book-awards.pages.dev/data/mcp/dataset-info.json.