Semantic Scholar Academic Research MCP
Provide AI assistants with seamless access to Semantic Scholar's extensive academic database for advanced paper discovery, author research, citation analysis, and content exploration. Enable natural language search, bulk data processing, and detailed academic insights to enhance research workflows. Empower users to efficiently explore millions of research papers and citation networks.
https://semantic-scholar-graph-api--alperenkocyigit.run.tools
How to connect
-
Smithery (hosted)
1. Open https://smithery.ai/servers/alperenkocyigit/semantic-scholar-graph-api 2. Click Connect and complete OAuth in your MCP client (Claude, Cursor, VS Code, etc.) 3. MCP endpoint: https://semantic-scholar-graph-api--alperenkocyigit.run.tools
Tools (12)
-
search_semantic_scholarSearch for papers on Semantic Scholar using a query string. Args: query: Search query string num_results: Number of results to return (default: 10) Returns: List of dictionaries containing paper information
-
get_semantic_scholar_paper_detailsGet details of a specific paper on Semantic Scholar. Args: paper_id: ID of the paper Returns: Dictionary containing paper details
-
get_semantic_scholar_author_detailsGet details of a specific author on Semantic Scholar. Args: author_id: ID of the author Returns: Dictionary containing author details
-
get_semantic_scholar_citations_and_referencesGet citations and references for a specific paper on Semantic Scholar. Args: paper_id: ID of the paper Returns: Dictionary containing lists of citations and references
-
search_semantic_scholar_authorsSearch for authors on Semantic Scholar using a query string. Args: query: Search query string for author names limit: Number of results to return (default: 10, max: 100) Returns: List of dictionaries containing author information
-
get_semantic_scholar_paper_matchFind the best matching paper on Semantic Scholar using title-based search. Args: query: Paper title or partial title to match Returns: Dictionary containing the best matching paper with match score
-
get_semantic_scholar_paper_autocompleteGet paper title autocompletion suggestions for a partial query. Args: query: Partial paper title query (will be truncated to 100 characters) Returns: List of dictionaries containing autocomplete suggestions
-
get_semantic_scholar_papers_batchGet details for multiple papers at once using batch API. Args: paper_ids: List of paper IDs (max 500) Returns: List of dictionaries containing paper details
-
get_semantic_scholar_authors_batchGet details for multiple authors at once using batch API. Args: author_ids: List of author IDs (max 1000) Returns: List of dictionaries containing author details
-
search_semantic_scholar_snippetsSearch for text snippets from papers that match the query. Args: query: Plain-text search query limit: Number of results to return (default: 10, max: 1000) Returns: List of dictionaries containing snippet matches with paper info
-
get_semantic_scholar_paper_recommendations_from_listsGet recommended papers based on lists of positive and negative example papers. Args: positive_paper_ids: List of paper IDs that represent positive examples (papers you like/want similar to) negative_paper_ids: Optional list of paper IDs that represent negative examples (papers you don't want similar to) limit: Number of recommendations to return (default: 10, max: 500) Returns: List of dictionaries containing recommended papers with relevance scores
-
get_semantic_scholar_paper_recommendationsGet recommended papers for a single positive example paper. Args: paper_id: ID of the paper to get recommendations for (positive example) limit: Number of recommendations to return (default: 10, max: 500) Returns: List of dictionaries containing recommended papers similar to the input paper