# Semantic Scholar AI integration on Definable > Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature ## What this connects Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature Vendor: https://www.semanticscholar.org ## Tools available **21** tools available. First 12: - `SEMANTICSCHOLAR_DETAILS_ABOUT_AN_AUTHOR` — Details about an author — Retrieve detailed information about an author from Semantic Scholar, including name, affiliations, publication statistics (paperCount, citationCount, h-index), external IDs (ORCID, DBLP), and optionally papers. By default returns authorId and name only. Use 'fields' parameter for additional data: name, url, affiliations, homepage, externalIds, paperCount, citationCount, hIndex, papers (supports nested fields like papers.title, papers.year). Limit: 10 MB per request. - `SEMANTICSCHOLAR_DETAILS_ABOUT_AN_AUTHOR_S_PAPERS` — Details about an author s papers — Retrieves a list of papers authored or co-authored by a specific researcher identified by their unique Semantic Scholar author ID. This endpoint is particularly useful for conducting literature reviews, analyzing an author's body of work, or tracking a researcher's publications over time. It provides a comprehensive view of an author's contributions to their field of study, including all papers where the author is listed as an author regardless of their authorship position. The response may be paginated for authors with a large number of publications, and additional API calls might be necessary to retrieve the complete list of papers. Use the offset and limit parameters to control pagination. - `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER` — Details about a paper — Examples: - `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER_S_AUTHORS` — Details about a paper s authors — Retrieves the list of authors for a specific paper identified by its unique paper_id in the Semantic Scholar database. This endpoint returns detailed author information including authorId and name (returned by default), and optionally: url, affiliations, homepage, paperCount, citationCount, hIndex, and papers (with subfields). Use the 'fields' parameter to request additional author fields beyond the defaults. The response is paginated and includes offset/limit parameters for retrieving large author lists. This tool is ideal for exploring paper collaborations, identifying author affiliations, or building author networks. It accepts various paper ID formats including Semantic Scholar IDs, DOI, ARXIV, PMID, and others. - `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER_S_CITATIONS` — Details about a paper s citations — Retrieves a list of citations for a specific academic paper using its unique Semantic Scholar paper ID. This endpoint is useful for researchers and developers who want to explore the impact and connections of a particular academic work within the broader scientific literature. It provides information about other papers that have cited the specified paper, allowing users to trace the influence of research and discover related works. The endpoint should be used when analyzing the reception and impact of a specific paper, building citation networks, or conducting bibliometric studies. It does not provide the full text of citing papers or detailed information about the citations beyond basic metadata. - `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER_S_REFERENCES` — Details about a paper s references — Retrieves the list of references cited by a specific paper in the Semantic Scholar database. This endpoint allows users to explore the scholarly context of a publication by accessing its bibliography. It's particularly useful for understanding the foundation of a paper's research, tracing the development of ideas, or conducting literature reviews. The tool returns details about the cited papers, which may include their titles, authors, publication dates, and Semantic Scholar IDs. It should be used when analyzing a paper's sources or investigating the connections between different academic works. Note that this endpoint only provides outgoing references (papers cited by the specified paper) and not incoming citations (papers that cite the specified paper). - `SEMANTICSCHOLAR_GET_DATASET` — Get dataset download links — Tool to get download links for a specific dataset within a release. Use when you need to download Semantic Scholar dataset files from S3. Returns pre-signed URLs for all dataset partitions. - `SEMANTICSCHOLAR_GET_DATASET_DIFFS` — Get dataset diffs — Get download links for incremental diffs between dataset releases. Returns a list of diffs required to update a dataset from start_release to end_release, enabling efficient dataset synchronization. Use when you need to update a local dataset copy without re-downloading the entire dataset. - `SEMANTICSCHOLAR_GET_DETAILS_FOR_MULTIPLE_AUTHORS_AT_ONCE` — Get details for multiple authors at once — Retrieves detailed information for multiple authors from Semantic Scholar in a single API call. This endpoint allows users to efficiently fetch data for a batch of authors by providing their unique Semantic Scholar IDs. It's particularly useful for applications that need to gather information on multiple authors simultaneously, reducing the number of individual API calls required. The endpoint accepts a list of author IDs and returns comprehensive details for each author, which may include their publications, citations, and other relevant academic information. While the exact response structure is not specified in the given schema, users can expect rich metadata about the requested authors. - `SEMANTICSCHOLAR_GET_DETAILS_FOR_MULTIPLE_PAPERS_AT_ONCE` — Get details for multiple papers at once — Retrieve detailed information for multiple academic papers in a single API call using the Semantic Scholar paper batch endpoint. This endpoint efficiently fetches data for up to 500 papers at once, significantly reducing the number of individual API requests needed. Key features: - Accepts multiple paper ID formats (Semantic Scholar ID, CorpusId, DOI, ArXiv, PMID, etc.) - Customizable field selection to retrieve only needed data - Papers not found return null in the corresponding array position - Results maintain the same order as input IDs - Supports nested field queries (e.g., authors.name, citations.title) Use this endpoint when you have a list of known paper IDs and want to retrieve their details simultaneously, rather than making individual requests for each paper. - `SEMANTICSCHOLAR_GET_PAPER_RECOMMENDATIONS` — Get paper recommendations — Tool to get paper recommendations based on positive and negative example papers. Use when you need to find papers similar to ones you like (positive examples) and optionally dissimilar to ones you don't like (negative examples). The recommendation engine analyzes the provided examples and returns relevant papers from the Semantic Scholar database. - `SEMANTICSCHOLAR_GET_RECOMMENDATIONS_FOR_PAPER` — Get recommendations for paper — Tool to get recommended papers for a single positive example paper. Use when you need to find papers similar to a given paper based on Semantic Scholar's recommendation algorithm. ## Auth Auth schemes: `API_KEY`. ## How agents use Semantic Scholar Inside a Definable workflow, Semantic Scholar is one of the tools the **Distributor specialist** can call. Example coordination patterns: - **Researcher → Semantic Scholar** — the Researcher (GPT-5.5) pulls context from Semantic Scholar (records, threads, documents), synthesises findings, and briefs the rest of the team. - **Writer → Distributor → Semantic Scholar** — the Writer (Claude Opus 4.7) drafts copy in brand voice, the Verifier passes it, then the Distributor writes the result into Semantic Scholar (create record, post message, draft email). - **Designer / Engineer → Distributor → Semantic Scholar** — the Designer ships an asset or the Engineer ships a code change, the Distributor delivers it via Semantic Scholar (attach file, open PR comment, post status). The Verifier checks every Semantic Scholar call. On rate limit, schema drift, or auth refresh it self-heals and retries — the workflow completes without manual intervention. ## Categories - artificial intelligence — https://definable.ai/apps/category/artificial-intelligence/ - analytics — https://definable.ai/apps/category/analytics/ ## Related - HTML page: https://definable.ai/apps/semanticscholar/ - Same category (artificial intelligence): https://definable.ai/apps/category/artificial-intelligence/ - All integrations: https://definable.ai/apps/ - Workflow (multi-agent loop): https://definable.ai/workflow/ - Apps llms.txt index: https://definable.ai/llms-apps.txt