Scopus Plus MCP vs Semantic Scholar MCP

Scopus Plus MCP tools

No tool names declared yet. Check the project README from the listing page.

Semantic Scholar MCP tools 14

• semantic_scholar_search_papersSearch for academic papers. Relevance-ranked keyword search over 200M+ papers; supports boolean operators (AND, OR, NOT) and quoted phrases, plus year, field-of-study, publication-type, open-access, and citation-count filters. Page with offset/limit (max 100 per call). For sorted or very large result sets use semantic_scholar_bulk_search; to search inside paper full text use semantic_scholar_snippet_search; to resolve one known title use semantic_scholar_match_paper. Returns Markdown by default, response_format='json' for raw JSON.
• semantic_scholar_get_paperGet paper details. Accepts: S2 ID, DOI:xxx, ARXIV:xxx, PMID:xxx, CorpusId:xxx Returns title, abstract, authors, venue, year, citation counts, TLDR, and open-access PDF link for one paper, e.g. paper_id='ARXIV:1706.03762'. Set include_citations / include_references to also list citing and referenced papers (fetched in parallel, 1-100 each). Results are cached in memory for 5 minutes; an unknown ID raises a not-found error. Unkeyed requests are throttled to 1 req/s (10 req/s with SEMANTIC_SCHOLAR_API_KEY) and 429/502/503 responses retry automatically with backoff. Returns Markdown by default, response_format='json' for raw JSON. To fetch many papers at once use semantic_scholar_bulk_papers.
• semantic_scholar_search_authorsSearch for academic authors by name. Example: query='Yoshua Bengio'. Several distinct researchers can share a name, so confirm identity with semantic_scholar_get_author (affiliations, h-index, publications) before attributing work; to list the authors of a specific paper use semantic_scholar_paper_authors instead. Page with offset/limit (max 100 per call, default 10). Returns Markdown by default, response_format='json' for raw JSON.
• semantic_scholar_get_authorGet author profile with optional publications list.
• semantic_scholar_recommendationsGet paper recommendations based on a seed paper. Provide one paper you already know (e.g. paper_id='ARXIV:1706.03762') and receive up to `limit` similar papers. from_pool picks the candidate pool: 'recent' (default, recently published papers from all fields) or 'all-cs' (computer-science papers of any age). When steering with several positive or negative examples, use semantic_scholar_multi_recommend instead. An unknown seed ID raises a not-found error; unkeyed requests are throttled to 1 req/s (10 req/s with SEMANTIC_SCHOLAR_API_KEY) and 429/502/503 responses retry automatically with backoff. Returns Markdown by default, response_format='json' for raw JSON.
• semantic_scholar_bulk_papersRetrieve multiple papers in a single request (max 500).
• semantic_scholar_bulk_searchSearch papers with sorting and cursor-based pagination for large result sets. Unlike regular search, supports sorting (e.g., by citation count) and returns a continuation token for paging through all results.
• semantic_scholar_export_citationExport a citation for a paper in BibTeX format. Use once you have a paper ID (from semantic_scholar_search_papers or semantic_scholar_match_paper), e.g. paper_id='DOI:10.18653/v1/N18-3011'. Returns the BibTeX entry as plain text - there is no response_format option. Raises an error for an unknown ID, a paper without citation data, or any format other than 'bibtex'.
• semantic_scholar_match_paperFind the single best paper matching a title string. Returns match score.
• semantic_scholar_paper_authorsGet full author profiles for a paper's authors. Unlike the abbreviated author list embedded in semantic_scholar_get_paper results, this returns each author's complete profile - affiliations, h-index, paper and citation counts - plus author IDs usable with semantic_scholar_get_author. Example: paper_id='DOI:10.18653/v1/N18-3011'. Authors are returned in listed order (limit 1-1000, default 100). Returns Markdown by default, response_format='json' for raw JSON.
• semantic_scholar_author_batchRetrieve multiple authors in a single request (max 1000).
• semantic_scholar_multi_recommendGet recommendations using multiple positive and negative example papers. Use instead of semantic_scholar_recommendations when steering with more than one example: results resemble positive_paper_ids and are pushed away from negative_paper_ids. Example: positive_paper_ids=['ARXIV:1706.03762', 'DOI:10.18653/v1/N19-1423'], negative_paper_ids=['ARXIV:1409.0473']. Accepts 1-100 positive and up to 100 negative IDs in any supported paper-ID format; malformed IDs raise an error before any request is made. Returns up to `limit` (1-500, default 10) papers, Markdown by default or response_format='json' for raw JSON.
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