# Semantic Scholar — how to use (mcp.ai)

Connect your Semantic Scholar account and use 21 tools for artificial intelligence straight from your AI agent. Connect with your own API key. Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature.

## Option A — via MCP (recommended)
Remote MCP endpoint (HTTP, streamable): `https://api.mcp.ai/p_semanticscholar?ms=1787295840000`
Add it as a custom/remote MCP connector in your client (Claude, Cursor, VS Code…), then authenticate when prompted. Once connected, ask the agent to use the server's tools (e.g. `semanticscholar_details_about_a_paper`).

## Option B — via direct REST API
Base URL: `https://api.mcp.ai/api/semanticscholar`
Auth: `Authorization: Bearer sk_live_…` — create a workspace API key at https://mcp.ai/settings/api-keys
Discover endpoints: `GET https://api.mcp.ai/api/semanticscholar/_endpoints`

### Endpoints
- `POST https://api.mcp.ai/api/semanticscholar/details/about/a/paper` — Examples: <ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b</code></li> <ul> <li>Returns a paper with its paperId and title. </li> </ul> <li><code>
  - body: { fields?: string, paper_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/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 (re
  - body: { limit?: integer, fields?: string, offset?: integer, paper_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/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 connec
  - body: { limit?: integer, fields?: string, offset?: integer, paper_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/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.
  - body: { limit?: integer, fields?: string, offset?: integer, paper_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/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 optional
  - body: { fields?: string, author_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/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 review
  - body: { limit?: integer, fields?: string, offset?: integer, author_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/get/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.
  - body: { release_id: string, dataset_name: string }
- `POST https://api.mcp.ai/api/semanticscholar/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. U
  - body: { dataset_name: string, end_release_id: string, start_release_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/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 Se
  - body: { ids: string[], fields?: string }
- `POST https://api.mcp.ai/api/semanticscholar/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, si
  - body: { ids?: string[], fields?: string }
- `POST https://api.mcp.ai/api/semanticscholar/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
  - body: { limit?: integer, fields?: string, negative_paper_ids?: string[], positive_paper_ids: string[] }
- `POST https://api.mcp.ai/api/semanticscholar/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.
  - body: { from?: string, limit?: integer, fields?: string, paper_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/get/release` — 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
  - body: { release_id: string }
- `POST https://api.mcp.ai/api/semanticscholar/list/releases` — Tool to list all available dataset releases from Semantic Scholar. Use when you need to discover available release dates for downloading datasets.
- `POST https://api.mcp.ai/api/semanticscholar/paper/relevance/search` — 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
  - body: { year?: string, limit?: integer, query: string, venue?: string, fields?: string, offset?: integer, fieldsOfStudy?: string, openAccessPdf?: string, minCitationCount?: integer, publicationTypes?: string, publicationDateOrYear?: string }
- `POST https://api.mcp.ai/api/semanticscholar/paper/title/search` — 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/g
  - body: { year?: string, query: string, venue?: string, fields?: string, fieldsOfStudy?: string, openAccessPdf?: string, minCitationCount?: integer, publicationTypes?: string, publicationDateOrYear?: string }
- `POST https://api.mcp.ai/api/semanticscholar/search/bulk/papers` — 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
  - body: { sort?: string, year?: string, query?: string, token?: string, venue?: string, fields?: string, fieldsOfStudy?: string, openAccessPdf?: string, minCitationCount?: integer, publicationTypes?: string, publicationDateOrYear?: string }
- `POST https://api.mcp.ai/api/semanticscholar/search/for/authors/by/name` — 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 (
  - body: { limit?: integer, query: string, fields?: string, offset?: integer }
- `POST https://api.mcp.ai/api/semanticscholar/search/papers` — 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 sco
  - body: { year?: string, limit?: integer, query: string, venue?: string, fields?: string, offset?: integer, fieldsOfStudy?: string, openAccessPdf?: string, minCitationCount?: integer, publicationTypes?: string, publicationDateOrYear?: string }
- `POST https://api.mcp.ai/api/semanticscholar/suggest/paper/query/completions` — 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
  - body: { query: string }
- `POST https://api.mcp.ai/api/semanticscholar/text/snippet/search` — Search for text snippets (~500 words) within academic papers that match your natural language query. Returns relevant excerpts from papers' titles, abstracts, and body text, ranked by relevance score.
  - body: { year?: string, limit?: integer, query: string, venue?: string, fields?: string, authors?: string, paperIds?: string, fieldsOfStudy?: string, minCitationCount?: string, publicationDateOrYear?: string }

## Example prompts
- "What can I do in Semantic Scholar?"
- "Show me a summary of my Semantic Scholar account"

## More
- Page: https://mcp.ai/semanticscholar
- Agent spec (llms.txt): https://mcp.ai/semanticscholar/llms.txt
- Postman collection: https://mcp.ai/semanticscholar/postman.json