Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature
Retrieve details for a paper by its ID, with selectable fields. By default the tool requests title, abstract, year, authors, citation and reference counts, URL, venue, publication date, and fields of study; responses are limited to 10 MB.
Retrieve a paginated list of authors for a Semantic Scholar paper, identified by a supported ID such as a Semantic Scholar ID, DOI, or arXiv ID. Author IDs and names are returned by default; additional author details and paper subfields can be requested. Use offset and limit to page through results, with up to 1,000 authors per page.
Retrieve a paginated list of papers citing a specified paper. Optional fields can include citation contexts, intents, influence indicators, and citing-paper metadata.
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).
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.
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.
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.
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.
Tool to retrieve metadata for a specific Semantic Scholar dataset release. Returns release information including available datasets with their descriptions. Use when you need to discover what datasets are available in a release or get release documentation.
Retrieve details for up to 1,000 Semantic Scholar authors by ID in one batch, with selectable author and paper fields. By default the API returns author IDs and names.
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.
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.
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.
Tool to list all available dataset releases from Semantic Scholar. Use when you need to discover available release dates for downloading datasets.
DEPRECATED: Use SEMANTICSCHOLAR_SEARCH_PAPERS instead. The SearchPapers endpoint allows users to search for academic papers within the Semantic Scholar database. It provides a powerful way to discover relevant scientific literature based on user-defined search criteria. This endpoint should be used when researchers, students, or developers need to find papers related to specific topics, authors, or time periods. The search functionality supports various query parameters to refine and customize the search results, making it suitable for both broad exploratory searches and targeted inquiries. However, users should be aware that the search is limited to papers indexed by Semantic Scholar, and very recent publications might not be immediately available. The endpoint returns a list of papers matching the search criteria, along with selected metadata fields, facilitating efficient literature review and analysis.
Behaves similarly to <code>/paper/search</code>, but is intended for retrieval of a single paper based on closest title match to given query. Examples: <ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/search/match?query=Construction of the Literature Graph in Semantic Scholar</code></li> <ul> <li>Returns a single paper that is the closest title match.</li> <li>Each paper has its paperId, title, and matchScore as well as any other requested fields.</li> </ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/search/match?query=totalGarbageNonsense</code></li> <ul> <li>Returns with a 404 error and a "Title match not found" message.</li> </ul> </ul> <br> Limitations: <ul> <li>Will only return the single highest match result.</li> </ul> </ul>
Tool to perform bulk search for academic papers. Intended for bulk retrieval of basic paper data without search relevance scoring. Use when you need to retrieve large sets of papers with optional text filtering and various criteria. Supports token-based pagination for efficient fetching of up to 10 million papers (use Datasets API for larger needs).
Search for academic authors in the Semantic Scholar database by name. This action searches for authors using plain-text name queries. The search is case-insensitive and supports partial name matches (e.g., "Smith" will match "John Smith", "Adam Smith", etc.). Use cases: - Find authors by their name to get their author ID - Discover authors in a specific research area by searching common names - Retrieve author metadata including publications, affiliations, citation counts, and h-index - Build author directories or research networks The response includes pagination metadata (total, offset, next) to help retrieve large result sets. Use the 'fields' parameter to customize which author attributes are returned, and use 'offset' and 'limit' for pagination through result sets larger than 1000 authors. Note: Results are paginated with a maximum of 1000 results per request. Use the 'next' field in the response to determine the offset for the next page.
Tool to search for academic papers by relevance in the Semantic Scholar database. Use when searching for papers on specific topics, keywords, or research areas. Returns papers ordered by relevance score with support for extensive filtering by publication type, date, venue, field of study, and citation metrics.
Get autocomplete suggestions for paper queries. Returns a list of papers matching the partial query string, useful for interactive search experiences. Each suggestion includes the paper ID, title, and authors with publication year. Example: For query "machine learning", returns papers like "Machine learning - a probabilistic perspective" by Murphy, 2012.
Search for text snippets matching a plain-text query in academic papers. Results are ranked by relevance and include paper information and a score; excerpts are drawn from titles, abstracts, and body text. Filter by author, year, or venue, choose snippet fields, and set a result limit (default 10, maximum 1,000).
The Semantic Scholar integration connects your Dench AI CRM directly to Semantic Scholar, so agents can read and act on your Semantic Scholar data as part of everyday work — answering questions in chat, keeping your CRM in sync, and running automations without anyone copying data between tools.
21 actions are available for agents to invoke on your behalf. Every call runs through Semantic Scholar's own authorization, scoped to the account you connect.
Sign in to your Dench workspace and open Integrations.
Find Semantic Scholar and click Connect — you'll authorize access through Semantic Scholar's own sign-in flow. No API keys or code required.
Ask an agent to use Semantic Scholar in chat, or call it from an automation.
Manage or disconnect the connection any time from workspace settings.
The Dench Semantic Scholar integration connects your AI CRM to Semantic Scholar, so AI agents can work with your Semantic Scholar data as part of chats, automations, and CRM workflows. You connect your account once, and every agent in your workspace can use it — governed by your workspace permissions.
The Semantic Scholar integration currently exposes 21 actions, including Details about a paper, Details about a paper s authors, Details about a paper s citations, Details about a paper s references, Details about an author, and Details about an author s papers. Agents invoke them on your behalf from chat or from automations.
No. You connect Semantic Scholar from your Dench workspace using Semantic Scholar's own sign-in and authorization flow — no API keys to copy, no glue code to maintain.
Connections are authorized through Semantic Scholar's own authentication flow, and Dench stores only the authorization needed to act on your behalf. You can review and disconnect the Semantic Scholar connection from your workspace settings at any time.