Semantic Scholar MCP Server

A Model Context Protocol (MCP) server that provides access to Semantic Scholar's academic paper database through their API.

Features

• Paper Search: Search for academic papers with filters for year, fields of study, and open access
• Paper Details: Get comprehensive information about specific papers including abstracts, authors, and citation counts
• Author Information: Retrieve detailed author data including affiliations, h-index, and citation metrics
• Citation Export: Generate citations in multiple formats (BibTeX, APA, MLA, Chicago)

Usage

Get API Key

While the Semantic Scholar API can be used without authentication, having an API key provides higher rate limits. To get an API key:

1. Visit Semantic Scholar API
2. Request an API key

Add to Claude Code configuration

Run the following command to add the Semantic Scholar MCP server to your project-scope Claude Code configuration:

claude mcp add semantic-scholar-mcp -s project -e SEMANTIC_SCHOLAR_API_KEY="your-api-key-here" -- uv run --with "git+https://github.com/FujishigeTemma/semantic-scholar-mcp" semantic-scholar-mcp serve

Or manually add it to your .mcp.json configuration file:

{
  "mcpServers": {
    "semantic-scholar-mcp": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "run",
        "--with",
        "git+https://github.com/FujishigeTemma/semantic-scholar-mcp",
        "semantic-scholar-mcp",
        "serve"
      ],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

1. search_paper - Search for papers

   • Required: query (search terms)
   • Optional: fields, limit, offset, year, fieldsOfStudy, openAccessPdf

2. get_paper - Get detailed paper information

   • Required: paper_id (supports multiple ID types: DOI, ArXiv ID, S2 Paper ID, etc.)
   • Optional: fields (customize returned data, see: Field Customization)

3. get_authors - Get author information for a paper

   • Required: paper_id
   • Optional: fields, limit, offset

4. get_citation - Generate formatted citations

   • Required: paper_id
   • Optional: format (bibtex, apa, mla, chicago)

CLI Examples

Search for papers:

semantic-scholar-mcp tools search_paper "machine learning" --limit 5 --year "2020-2023"

Get paper details:

semantic-scholar-mcp tools get_paper "10.1038/nature12373"

Get authors for a paper:

semantic-scholar-mcp tools get_authors "649def34f8be52c8b66281af98ae884c09aef38b"

Generate BibTeX citation:

semantic-scholar-mcp tools get_citation "649def34f8be52c8b66281af98ae884c09aef38b" --format bibtex

Field Customization

All tools support a fields parameter to customize the returned data. This allows you to request only the information you need, reducing response size and improving performance.

Paper Fields (for search_paper and get_paper)

Basic fields:

• paperId - Unique paper identifier
• title - Paper title
• abstract - Paper abstract
• year - Publication year
• publicationDate - Full publication date

Author information:

• authors - List of authors (returns authorId and name by default)
• authors.authorId - Author's unique identifier
• authors.name - Author's name
• authors.affiliations - Author's institutional affiliations
• authors.citationCount - Author's total citation count
• authors.hIndex - Author's h-index

Citation and reference data:

• citationCount - Number of times this paper has been cited
• referenceCount - Number of references in this paper
• citations - List of papers that cite this paper
• references - List of papers referenced by this paper

Publication details:

• journal - Journal information (name, volume, pages, etc.)
• venue - Publication venue
• publicationTypes - Types of publication (e.g., JournalArticle, Conference)
• fieldsOfStudy - Academic fields (e.g., Computer Science, Medicine)
• s2FieldsOfStudy - Semantic Scholar's field classifications

Additional metadata:

• doi - Digital Object Identifier
• arxivId - ArXiv identifier
• url - Paper URL
• openAccessPdf - Open access PDF information
• embedding - Paper embedding vectors (for similarity analysis)

Author Fields (for get_authors)

• authorId - Unique author identifier
• name - Author's name
• affiliations - Institutional affiliations
• citationCount - Total citation count
• hIndex - h-index metric
• paperCount - Number of papers published
• url - Author's profile URL

Example Field Usage

Get basic paper information:

semantic-scholar-mcp tools search_paper "machine learning" --fields "paperId,title,year,citationCount"

Get detailed paper with author affiliations:

semantic-scholar-mcp tools get_paper "10.1038/nature12373" --fields "title,abstract,authors.name,authors.affiliations,journal,year"

Get comprehensive author information:

semantic-scholar-mcp tools get_authors "649def34f8be52c8b66281af98ae884c09aef38b" --fields "authorId,name,affiliations,citationCount,hIndex,paperCount"

Development

Setting up the development environment

uv sync

uv run pytest tests/
uv run ruff format .
uv run ruff check . --fix
uv run ty check

Project Structure

semantic-scholar-mcp/
  src/
    semantic_scholar_mcp/
      __init__.py
      server.py      # Main server implementation
      cli.py         # CLI interface
  tests/                 # Test files
  pyproject.toml        # Project configuration
  README.md            # This file

API Rate Limits

• Without API key: 100 requests per 5 minutes
• With API key: 1 request per second (higher limits available on request)

Supported Paper ID Types

The API supports various paper identifier formats:

• Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b")
• DOI (e.g., "10.1038/nature12373")
• ArXiv ID (e.g., "arXiv:2106.15928")
• MAG ID
• ACL ID
• PubMed ID
• Corpus ID

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

This project uses the Semantic Scholar API to access academic paper data.