Open Library's Book Worm system currently depends on Amazon and ISBNdb for metadata, creating gaps for books with missing or incomplete records. This leads to failed imports and poor-quality placeholder entries, particularly affecting international and rare titles. The proposal adds Google Books as a fallback metadata source to address these data quality issues.

When Amazon lookups fail or only ISBN-13 identifiers are available, Book Worm will query the Google Books API to supplement missing information. This fallback mechanism requires both high_priority=true and stage_import=true parameters to activate, ensuring controlled staging of enriched records. The integration targets specific edition fields including title, subtitle, authors, publishers, publish date, page count, and description.

Technical implementation involves updating STAGED_SOURCES in imports.py and creating new functions in affiliate_server.py for fetching and processing Google Books data. The system must handle edge cases like multiple matches or missing fields, logging warnings when Google Books returns ambiguous results to prevent unreliable data introduction.

This enhancement directly improves Open Library's import success rates and reduces placeholder entries like 'Book 978...' by providing richer metadata through the Google Books API. The measurable impact includes higher data completeness and better user trust in the import experience.