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741,982 tools. Updated 2026-11-04 11:36

"A tool for finding academic papers using semantic search and citation analysis" matching MCP tools:

  • Find papers that CITE a given article — forward citation search. Pass one PMID; returns citing papers (most recent first) with full citation metadata. Use for "who cited this", "has this finding been replicated or challenged", or tracking a paper's downstream impact. NOTE: coverage is the PubMed Central citation graph (open-access + participating publishers), so the count is a FLOOR, not the paper's total citation count (for that, a tool like Semantic Scholar / OpenAlex covers more). Distinct from pubmed_get_related_articles (similar papers, not citing papers).
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  • Search 200M+ academic papers on Semantic Scholar by keyword or exact title. Returns titles, authors, year, venue, CITATION COUNTS, DOI, and open-access PDF links. PREFER for "how many citations does <paper title> have", "citation count for <paper>", "how cited is <paper>" — search the title and read citationCount off the match. Optionally filter by year range and field of study. Keyless.
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  • Search academic papers, books, and datasets via Crossref. For "search works titled X" / finding a SPECIFIC paper by its title, pass `title` (title-scoped — ranks the exact paper first). Beyond keyword search you can SORT by citation count or publication date and FILTER by date range, work type, author, and JOURNAL (`journal` = a journal name like "Progress in Materials Science" or an ISSN; results are restricted to that journal only) — e.g. "most-cited papers on transformers" (sort=citations), "papers on LLMs since 2024" (from_date=2024-01-01), "recent journal articles by an author". Returns titles, authors, journal, DOIs, citation counts, and dates.
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  • Research intel — has someone solved X already? Queries 240M+ academic works via OpenAlex (includes arXiv preprints, conference papers, journal articles), ranks by citation count + recency + relevance, returns top N papers with one-line abstract excerpts, citation counts, and author names. Built for autonomous agents that need to check prior art before burning cycles re-deriving a known result. Fallback to Semantic Scholar. (price: $0.05 USDC, tier: metered)
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  • Search for academic papers and return BibTeX citations. Searches DBLP (computer science) and Semantic Scholar (broad academic coverage) for comprehensive results. Returns formatted BibTeX entries ready to use in LaTeX documents.
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  • Semantic Scholar cited-by lookup: list the papers that CITE a given paper (the works citing it), with their titles, authors, year, and citation counts. Use for "cited-by papers", "who cites this paper", "papers that cite X", forward citation tracing, and finding follow-up work. Keyless.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables agents to search, analyze, and explore arXiv academic papers with advanced multi-field search, author lookup, category browsing, citation extraction, and bibliography export.
    39 npm
    Apache 2.0
  • A
    license
    A
    quality
    D
    maintenance
    Enables searching academic literature via Semantic Scholar with relevance filters, batch metadata retrieval, and citation-graph recommendations.
    5
    AGPL 3.0

Matching MCP Connectors

  • Search research, revise drafts, format references and export your Citation documents.

  • Academic paper search, scientific literature, citation analysis, arXiv & semantic related-work.

  • Search 200M+ academic papers by title and abstract text across all fields of study. Returns Semantic Scholar paper ID, DOI, title, year, venue, authors, citation count, and open-access PDF link when available. Complements openalex.works_search (broader topic classification) with Semantic Scholar's AI-driven relevance ranking and TLDR summaries. Call semanticscholar.get_paper with a result paper_id, DOI, or ArXiv ID for full detail including abstract and TLDR. Data: api.semanticscholar.org, no auth required.
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  • Search open-access research papers by keyword (e.g., "machine learning healthcare"). Returns titles, authors, abstracts, DOIs, and download links. Useful for literature reviews and finding academic sources.
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  • Google Scholar academic search. Returns papers with snippets, publication info, and citation counts. Price: $0.005
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  • Search 226M+ academic papers across all fields with AI-ranked relevance via Semantic Scholar Graph API.
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  • Search academic papers via OpenAlex (default) or Semantic Scholar. Use source="semantic_scholar" for broader AI/CS preprint coverage and citation-influence metrics (#3596).
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.
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  • Semantic search over academic references by title + abstract, across languages. Only references with an abstract (about half) are covered; use search_references for the rest. Runs the corpus model, EmbeddingGemma 2, locally with no API key (first use downloads ~350 MB). similarity_score only ranks within one query.
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  • Semantic passage search over a neural embedding index: describe what you are looking for in natural language and get the closest passages, each with a permanent chunk_id, book/chapter/paragraph, citation, license and source URL. When to use: keyword/BM25 search misses paraphrases or themes ("a story about a soldier returning from war", "清代公案小说里的侠客"). Do not use: to verify an exact quotation (cyberlibrary_verify_quote) or to read a whole book (cyberlibrary_get_chunks). Falls back to BM25 if the embedding service is down. Returns: {query, lang, backend, total_vectors, corpus_version, results[{chunk_id, score, title, author, chapter, para, citation, url, license, snippet}]}.
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  • Search the Mississippi Code of 1972 BY TOPIC or keyword rather than citation — "security deposit", "concealed carry", "DUI per se limit", "homicide". Matches against every section's official CATCHLINE across the whole Code (not full statutory body text — there is no full-text search anywhere in this source chain; the official LexisNexis tool, Justia, and FindLaw all sit behind the same Cloudflare bot-check, confirmed live). Returns matching citations, titles, and catchlines; call ms_code_section with a citation for the full text. Keyless.
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  • Search the Ohio Revised Code BY TOPIC or keyword rather than by section number -- "EDGE certification minority business", "county competitive bidding threshold", "OVI penalties", "landlord security deposit". Covers all chapters of the Revised Code. Returns ranked sections with citation, heading and an opening excerpt; call oh_revised_code with a result's section for the full text. Full-text match, not semantic search. A query with no hits returns found:false. Keyless.
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