Semantic Scholar MCP Server

A comprehensive 14-tool MCP server for Semantic Scholar academic research workflows. Direct access to 200M+ papers from Semantic Scholar within Claude Desktop.

Installation

Option 1: One-Line Install (Recommended)

# No cloning needed — runs directly from PyPI
uvx s2-mcp-server

Option 2: Claude Code

claude mcp add semantic-scholar -- uvx s2-mcp-server

Option 3: Claude Desktop (Windows)

Add to %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "semantic-scholar": {
      "command": "uvx",
      "args": ["s2-mcp-server"],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "your-key-here"
      }
    }
  }
}

Option 4: Claude Desktop (macOS)

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "semantic-scholar": {
      "command": "uvx",
      "args": ["s2-mcp-server"],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "your-key-here"
      }
    }
  }
}

Option 5: pip / From Source

pip install s2-mcp-server
# or
git clone https://github.com/smaniches/semantic-scholar-mcp.git
cd semantic-scholar-mcp && pip install -e .

Note: Get a free API key at semanticscholar.org/product/api. Without a key, you get rate-limited public access (1 req/sec).

Configuration

API Key Options

You can provide your API key in two ways:

1. Environment Variable (recommended for persistent use):

   export SEMANTIC_SCHOLAR_API_KEY="your-api-key-here"

2. Per-Request Parameter (overrides env var):

   {
     "api_key": "your-api-key-here"
   }

   Caution: per-request api_key values are part of the tool-call arguments and may be visible in MCP transcripts, client logs, and the LLM's tool-call history depending on the client. For production use, prefer the SEMANTIC_SCHOLAR_API_KEY environment variable. Removal of the per-request parameter is tracked for v1.3.0.

Get a free API key at: https://www.semanticscholar.org/product/api

Claude Desktop Setup

Add to your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "semantic-scholar": {
      "command": "python",
      "args": ["-m", "semantic_scholar_mcp"],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "your-api-key-here"
      }
    }
  }
}

Then restart Claude Desktop.

Supported ID Formats

The server accepts the following paper identifier formats:

Tools Reference

1. semantic_scholar_search_papers

Search for academic papers with advanced filters.

Parameters:

Example:

Search for "transformer attention mechanism" papers from 2023 with at least 100 citations

JSON Example:

{
  "query": "transformer attention mechanism",
  "year": "2023",
  "min_citation_count": 100,
  "fields_of_study": ["Computer Science"],
  "limit": 20
}

2. semantic_scholar_get_paper

Get detailed information about a specific paper.

Parameters:

Example:

Get details for DOI:10.1038/s41586-021-03819-2 including its top 20 citations

JSON Example:

{
  "paper_id": "DOI:10.1038/s41586-021-03819-2",
  "include_citations": true,
  "citations_limit": 20
}

3. semantic_scholar_search_authors

Search for academic authors by name.

Parameters:

Example:

Find author "Yoshua Bengio"

JSON Example:

{
  "query": "Yoshua Bengio",
  "limit": 5
}

4. semantic_scholar_get_author

Get author profile with publications.

Parameters:

Example:

Get author profile for author ID 1741101 with their top 50 publications

JSON Example:

{
  "author_id": "1741101",
  "include_papers": true,
  "papers_limit": 50
}

5. semantic_scholar_recommendations

Get AI-powered paper recommendations based on a seed paper.

Parameters:

Example:

Get recommendations based on paper 649def34f8be52c8b66281af98ae884c09aef38b

JSON Example:

{
  "paper_id": "ARXIV:1706.03762",
  "limit": 15
}

6. semantic_scholar_bulk_papers

Retrieve multiple papers in a single request (max 500).

Parameters:

Example:

Retrieve these papers: DOI:10.1038/nature12373, ARXIV:2106.15928, PMID:32908142

JSON Example:

{
  "paper_ids": [
    "DOI:10.1038/nature12373",
    "ARXIV:2106.15928",
    "PMID:32908142"
  ]
}

7. semantic_scholar_bulk_search

Search papers with sorting and cursor-based pagination for large result sets. Unlike search_papers, supports a sort order and returns a token for paging through all results.

Parameters:

JSON Example:

{
  "query": "graph neural networks",
  "sort": "citationCount:desc",
  "year": "2020-2024",
  "limit": 100
}

Returns: total result count, the page of papers, and a token for the next page (when more results exist).

8. semantic_scholar_export_citation

Export a citation for a paper in BibTeX format.

Parameters:

JSON Example:

{
  "paper_id": "DOI:10.1038/s41586-021-03819-2",
  "format": "bibtex"
}

Returns: the BibTeX string for the requested paper.

9. semantic_scholar_match_paper

Find the single best paper matching a title string. Returns a numeric matchScore alongside the matched paper.

Parameters:

JSON Example:

{
  "query": "Attention Is All You Need"
}

Returns: the best-matching paper plus its matchScore, or "No matching paper found." if no match.

10. semantic_scholar_paper_authors

Get full author profiles for a paper's authors (richer than the abbreviated author list returned by get_paper).

Parameters:

JSON Example:

{
  "paper_id": "ARXIV:1706.03762",
  "limit": 25
}

Returns: the list of full author records for the paper.

11. semantic_scholar_author_batch

Retrieve multiple authors in a single request (max 1000).

Parameters:

JSON Example:

{
  "author_ids": ["1741101", "40348417", "144749327"]
}

Returns: counts of requested / retrieved, the retrieved author records, and a not_found list of IDs the API did not return.

12. semantic_scholar_multi_recommend

Get recommendations using multiple positive (and optional negative) example papers.

Parameters:

JSON Example:

{
  "positive_paper_ids": ["ARXIV:1706.03762", "ARXIV:1810.04805"],
  "negative_paper_ids": ["DOI:10.1038/nature14539"],
  "limit": 20
}

Returns: the recommended papers plus an echo of the positive/negative seeds used.

13. semantic_scholar_snippet_search

Search within paper full text and return text snippets with surrounding context. Heavily rate-limited without an API key.

Parameters:

JSON Example:

{
  "query": "scaling laws for language models",
  "year": "2022-2024",
  "limit": 20
}

Returns: matching snippets, each with the source paper title, section, and a short text excerpt.

14. semantic_scholar_status

Check server health and API connectivity status.

Parameters: None

Example:

Check Semantic Scholar API status

Response:

{
  "server": "semantic-scholar-mcp",
  "version": "1.2.2",
  "api_key_configured": true,
  "timestamp": "2026-04-06T12:00:00.000000+00:00",
  "api_reachable": true
}

Rate Limits

The server automatically handles rate limiting with:

• Request serialization to enforce minimum intervals

• Exponential backoff retry for 429 (rate limit) and 503 (service unavailable) errors

• Maximum 3 retries with jitter

Architecture

+-----------------+     +----------------------+     +-----------------+
|  Claude Desktop |---->|  semantic-scholar-mcp |---->| Semantic Scholar|
|   (MCP Client)  |<----|     (This Server)     |<----+      API        |
+-----------------+     +----------------------+     +-----------------+
        |                         |                          |
        | stdio (JSON-RPC)        | Your API Key             | HTTPS
        | Local process           | Local machine            | 200M+ papers

Where your API key goes. The MCP server runs locally on your machine and does not store your API key on disk. When the server makes authenticated requests, the key is sent only to api.semanticscholar.org over HTTPS as the x-api-key header that the Semantic Scholar API requires. No telemetry is sent to any third party. See the per-request api_key caution above for how transcript exposure can occur when the parameter is used per-request instead of via the environment variable.

Development

# Clone
git clone https://github.com/smaniches/semantic-scholar-mcp.git
cd semantic-scholar-mcp

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=src/semantic_scholar_mcp --cov-report=term-missing

# Type checking
mypy src/

Security

API keys are never persisted to disk by the server. Prefer the SEMANTIC_SCHOLAR_API_KEY environment variable over the per-request api_key tool parameter (see SECURITY.md for details on the transcript-exposure risk). All API communication uses HTTPS to api.semanticscholar.org. See SECURITY.md for vulnerability reporting and the v1.2.x known-limitations list.

License

MIT License - see LICENSE file.

Author

Santiago Maniches

• Founder & CEO, TOPOLOGICA LLC

• ORCID: 0009-0005-6480-1987

• LinkedIn: santiago-maniches

• Website: topologica.ai

Contributing

Contributions welcome! Please read our Contributing Guidelines.

Support

• Issues: GitHub Issues

• Discussions: GitHub Discussions

• Contact: santiago@topologica.ai