Source Library MCP Server

READ PIPELINE step 1 — DISCOVER. START HERE for any named work or author. Returns the book's AI-generated summary, chapter list, edition metadata, DOI, page counts, and IIIF manifest. The summary is typically a multi-paragraph orientation covering the book's argument, structure, and significance — often answering the question without further searching. Then: get_book_text to read a chapter or page range (step 2), get_quote / get_quotes to lock specific pages with full citation apparatus (step 3). search_within_book locates passages inside this book.

READ PIPELINE step 2 — READ. Read a book's text. Call get_book first (step 1) for the chapter list, then come here. Preferred: use the chapter param to read one chapter at a time (includes [Page N] markers for citation). Alternatively, use from/to for explicit page ranges (e.g. from=1 to=50). When you find passages worth quoting, hand the page numbers to get_quote / get_quotes (step 3) for verbatim text + a citation link. TRUNCATION: the response always includes truncated: true/false. When truncated=true, the truncation_note field gives the exact next from/to values to call — this means content was cut short by a page-budget limit, NOT that the book ended. An AI agent MUST NOT infer end-of-book from pages_returned alone; check truncated first. Budget limits apply to anonymous callers (~50 pages per 24h); sign in at sourcelibrary.org/auth/signin or get an API key at sourcelibrary.org/developers for higher limits.

BROWSES/FILTERS THE CATALOG by metadata (author/title fragment, language, category, translation recency) — no content/topic matching. PICK THIS to see WHAT EXISTS by an author or in a tradition. Returns books with title, author, language, year, and translation progress. → For a relevance-ranked topic search use search_library; for passages on a theme use search_translations (exact words) or search_concept (by meaning).

RETURNS QUOTABLE PASSAGES matched by MEANING (cosine similarity on Gemini embeddings, 768d) — paraphrases and adjacent phrasings match even with zero keyword overlap. PICK THIS when the modern term won't literally appear in historical texts — e.g. "distributed cognition" maps to passages about active intellect, art of memory, wax tablet metaphors; "social contract" maps to pre-Hobbesian discussions of consent and authority. → For exact words/distinctive terms use search_translations (cheaper, more precise); to list which BOOKS cover a topic use search_library; if the user named an author/work, get_book first (semantic search is expensive — reserve it for cross-corpus discovery). Similarity calibration: 0.70+ is a strong match, 0.55–0.70 is worth reading but verify, below 0.55 is mostly conceptual drift. Set max_per_book to diversify results across many books rather than cluster on one source. Each passage carries a snippet_type — quote only "translation" snippets, never "summary". Cross-cultural tip: for pre-modern or non-Western topics, also try source-tradition vocabulary — e.g. for seminal economy try "jing preservation" or "bindu yoga" or "istimnāʾ"; for masturbation try "mollities" (Latin) or "hastamaithuna" (Sanskrit) or "shouyin" (Chinese). The corpus is indexed via period translations that use tradition-internal terminology, so adjacent/euphemistic terms often surface material that modern English keywords miss.

Search 110,000+ historical illustrations, emblems, engravings, diagrams, AND 23,000+ artworks (paintings, prints, sculptures). Filter by type, subject, figure, symbol, year.

RETURNS A LIST OF BOOKS (works on a topic) — NOT passages. PICK THIS to discover which works exist on a subject. → For quotable text use search_translations (exact words) or search_concept (by meaning); if the user already named an author/work, call get_book directly (or list_books to find the ID) — the AI summary + chapter outline is usually the right first answer. Searches titles, authors, subjects, and (as a secondary signal) translated text. Query tips: single distinctive words or short phrases work best ("memory palace", "ouroboros"); quoted phrases match exactly. Each result includes total_matches (full count) + returned (this page) + offset for pagination.

Propose a themed collection of books to the Source Library team — a title, a rationale (why these books belong together and what thread connects them), and an ordered list of book ids. Get book ids from search_library / list_books / get_book. Like submit_feedback and share_findings, this goes to the team for REVIEW — it does NOT create a public collection instantly; a curator reviews and approves it. Use this when the user has identified a coherent set of books worth grouping and wants to contribute that curation back.

READ PIPELINE step 3 — CITE. Get the exact verbatim text of a single page plus its citation apparatus. ALWAYS use before putting text in quotation marks. The response headline is citation_link (the stable sourcelibrary.org/q/… shortlink) — present it to the user alongside the quote. Render as: > [exact translation text, verbatim] > — [Author], p. [N]. [citation_link] For several pages of one book at once, use get_quotes.

READ PIPELINE step 3 — CITE, in batch. Get verbatim text + citation_link for SEVERAL pages of a single book in one round-trip, to assemble a multi-passage dossier. Specify either pages (an explicit array, e.g. [12, 40, 41]) or an inclusive from/to range. Max 25 pages per call. Each entry carries its own citation_link to present alongside the quote.

Share a research dossier back to the Source Library team — a title, an optional summary, and an ordered list of citations (the passages your thesis rests on). Each citation is a reference { book_id, page, note }, NOT copied text: the library re-renders the canonical quote from the reference, so links stay authoritative. Use this when the user has assembled a thesis backed by passages across one or more books and wants to contribute it back. Like submit_feedback, this goes to the team for review (not an instant public page).

Submit feedback, bug reports, or feature requests to the Source Library team.

RETURNS QUOTABLE PASSAGES (page-level snippets + citation URLs), matched by KEYWORD/term. PICK THIS to find a quote or textual evidence on a topic across the whole library. → If the modern word won't literally appear in historical texts, use search_concept (matches by meaning); to list which BOOKS cover a topic use search_library; to dig inside one known book use search_within_book; if the user named an author/work, get_book first (its AI summary is usually the right first read). Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.

SEARCHES INSIDE ONE BOOK (requires book_id) — runs keyword AND scoped semantic search in parallel over that book's pages and merges them, so it handles both literal terms ("ouroboros") and conceptual queries ("the marriage of opposites"). PICK THIS once you have a candidate book and want every relevant page in it. → To find the book first, use search_library or search_concept (then pass its book_id here). Faster than a global re-search because it's scoped to one book's 100-500 pages. Returns OCR and translation snippets with page numbers, ready to cite.