s2-mcp-server 1.3.1

Model Context Protocol server for Semantic Scholar — 200M+ academic papers, 14 tools spanning paper search, citation graph traversal, author profiles, and recommendations.

Semantic Scholar MCP Server

A comprehensive 14-tool MCP server for Semantic Scholar academic research workflows. Direct access to 200M+ papers from Semantic Scholar — paper search, citation graph traversal, author profiles, and recommendations — from any Model Context Protocol client (e.g., Claude Desktop, Claude Code, Cursor, Cline, Continue, and others).

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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 .

Option 6: Docker

docker pull ghcr.io/smaniches/semantic-scholar-mcp:latest
docker run -e SEMANTIC_SCHOLAR_API_KEY=your-key ghcr.io/smaniches/semantic-scholar-mcp

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

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Architecture

flowchart LR
  Client["MCP client<br/>(Claude Desktop, Claude Code,<br/>Cursor, Cline, Continue, …)"]
  subgraph Server ["s2-mcp-server (this package)"]
    direction TB
    FastMCP["FastMCP runtime<br/>(stdio transport, lifespan)"]
    Tools["14 @mcp.tool functions<br/>(server.py)"]
    Models["Pydantic input models<br/>+ field sets (models.py)"]
    Validators["Paper-ID validator<br/>(validators.py)"]
    Cache["TTL cache<br/>(cache.py)"]
    Fmt["Markdown formatters<br/>(formatters.py)"]
    HTTP["httpx client<br/>+ rate limit + retry/backoff<br/>(client.py)"]
    Errors["Typed exceptions<br/>(errors.py)"]
    Log["Structured JSON logger<br/>(logging_config.py)"]
  end
  S2Graph["Semantic Scholar<br/>Graph API"]
  S2Recs["Semantic Scholar<br/>Recommendations API"]

  Client <-- "stdio (JSON-RPC)" --> FastMCP
  FastMCP --> Tools
  Tools --> Models
  Tools --> Validators
  Tools --> Cache
  Tools --> HTTP
  Tools --> Fmt
  HTTP --> Errors
  HTTP --> Log
  HTTP -- "GET / POST<br/>x-api-key" --> S2Graph
  HTTP -- "GET / POST<br/>x-api-key" --> S2Recs

Module responsibilities (src/semantic_scholar_mcp/):

Module	Responsibility
server.py	FastMCP instance, 14 @mcp.tool registrations, lifespan, main() entry. Re-exports the helper surface for back-compat.
client.py	Shared httpx.AsyncClient singleton, per-tier rate limiter (1 req/s public, 10 req/s keyed), retry loop with exponential backoff + jitter on 429/503/timeout, HTTP→typed-exception mapping.
models.py	Pydantic input models per tool, ResponseFormat enum, the four tiered field-set constants (PAPER_SEARCH_FIELDS, …_LITE, PAPER_BULK_SEARCH_FIELDS, PAPER_DETAIL_FIELDS, AUTHOR_FIELDS).
validators.py	Pre-flight paper-ID validation. Rejects NUL bytes, ?, #, path traversal; accepts the seven canonical ID formats.
cache.py	In-memory TTL cache (5 min, 200 entries, oldest-first eviction) for paper/author lookups within a session.
formatters.py	Markdown renderers for paper and author dicts, tuned for chat-surface readability.
errors.py	SemanticScholarError hierarchy: AuthenticationError, RateLimitError, NotFoundError, ValidationError, ServerError.
logging_config.py	One-JSON-per-line StructuredFormatter on stderr; safe to ship through any log aggregator.

Design choices worth knowing

• Single httpx.AsyncClient per process. Created lazily, closed in the FastMCP lifespan teardown. Amortizes connection setup; respects keep-alive limits.
• Rate limit is enforced at the client, not the API. A semaphore + last-request timestamp ensures we never exceed the per-tier interval even when the MCP host issues tool calls in parallel.
• Retry is bounded and jittered. Up to MAX_RETRIES = 3, base 1 s, capped at 30 s. Honors Retry-After when present.
• Errors are typed. Status codes map onto a small exception hierarchy so callers can branch on AuthenticationError vs RateLimitError vs NotFoundError instead of parsing strings.
• Input validation is pre-flight. Paper IDs are checked before any outbound request; bad IDs never hit the wire.
• Version is single-source. __version__ is derived from importlib.metadata.version("s2-mcp-server"), so bumping pyproject.toml is sufficient; release-please bumps the manifest, server.json (×2 paths), CITATION.cff, and .zenodo.json in lockstep on every release.

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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 planned for a follow-up release; see .github/SECURITY.md for the tracked list.

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.

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Supported ID Formats

The server accepts the following paper identifier formats:

Format	Pattern	Example
Semantic Scholar ID	40-character hex	649def34f8be52c8b66281af98ae884c09aef38b
DOI	DOI:xxx	DOI:10.1038/s41586-021-03819-2
ArXiv	ARXIV:xxx	ARXIV:2106.15928 or ARXIV:2106.15928v2
PubMed	PMID:xxx	PMID:32908142
Corpus ID	CorpusId:xxx	CorpusId:215416146
ACL	ACL:xxx	ACL:P19-1285
URL	URL:xxx	URL:https://arxiv.org/abs/2106.15928

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Tools Reference

1. semantic_scholar_search_papers

Search for academic papers with advanced filters.

Parameters:

Parameter	Type	Required	Description
query	string	Yes	Search query (supports AND, OR, NOT operators and "phrase search")
year	string	No	Year filter: "2024", "2020-2024", or "2020-"
fields_of_study	string[]	No	Filter by fields: ["Computer Science", "Biology"]
publication_types	string[]	No	Filter by type: ["Review", "JournalArticle"]
open_access_only	boolean	No	Only return open access papers (default: false)
min_citation_count	integer	No	Minimum citation count
limit	integer	No	Max results 1-100 (default: 10)
offset	integer	No	Pagination offset (default: 0)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
paper_id	string	Yes	Paper ID in any supported format
include_citations	boolean	No	Include citing papers (default: false)
include_references	boolean	No	Include referenced papers (default: false)
citations_limit	integer	No	Max citations to return 1-100 (default: 10)
references_limit	integer	No	Max references to return 1-100 (default: 10)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
query	string	Yes	Author name to search
limit	integer	No	Max results 1-100 (default: 10)
offset	integer	No	Pagination offset (default: 0)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

Example:

Find author "Yoshua Bengio"

JSON Example:

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

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4. semantic_scholar_get_author

Get author profile with publications.

Parameters:

Parameter	Type	Required	Description
author_id	string	Yes	Semantic Scholar author ID
include_papers	boolean	No	Include publications (default: true)
papers_limit	integer	No	Max papers to return 1-100 (default: 20)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
paper_id	string	Yes	Seed paper ID in any supported format
limit	integer	No	Max recommendations 1-100 (default: 10)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
paper_ids	string[]	Yes	List of paper IDs (max 500)
response_format	string	No	"markdown" or "json" (default: json)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
query	string	Yes	Search query
sort	string	No	Sort order, e.g. "citationCount:desc", "publicationDate:asc"
token	string	No	Continuation token from a previous bulk_search response
year	string	No	Year filter: "2024", "2020-2024", "2020-"
fields_of_study	string[]	No	Filter by fields: ["Computer Science"]
publication_types	string[]	No	Filter by type: ["Review", "JournalArticle"]
min_citation_count	integer	No	Minimum citation count
limit	integer	No	Max results per page 1-1000 (default: 100)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
paper_id	string	Yes	Paper ID in any supported format
format	string	No	Citation format (currently only "bibtex")
api_key	string	No	Override environment API key

JSON Example:

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

Returns: the BibTeX string for the requested paper.

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9. semantic_scholar_match_paper

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

Parameters:

Parameter	Type	Required	Description
query	string	Yes	Paper title to match (1-500 chars)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

JSON Example:

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

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

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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:

Parameter	Type	Required	Description
paper_id	string	Yes	Paper ID in any supported format
limit	integer	No	Max authors to return 1-1000 (default: 100)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
author_ids	string[]	Yes	List of author IDs (1-1000)
response_format	string	No	"markdown" or "json" (default: json)
api_key	string	No	Override environment API key

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:

Parameter	Type	Required	Description
positive_paper_ids	string[]	Yes	Papers to find similar results for (1-100)
negative_paper_ids	string[]	No	Papers to dissimilate from (0-100)
limit	integer	No	Max recommendations 1-500 (default: 10)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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.

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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:

Parameter	Type	Required	Description
query	string	Yes	Search query for paper text (1-500 chars)
paper_ids	string[]	No	Limit search to specific papers (max 100)
year	string	No	Year filter: "2024", "2020-2024", "2020-"
fields_of_study	string[]	No	Filter by fields: ["Computer Science"]
min_citation_count	integer	No	Minimum citation count
limit	integer	No	Max results 1-100 (default: 10)
response_format	string	No	"markdown" or "json" (default: markdown)
api_key	string	No	Override environment API key

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

Tier	Requests/Second	How to Get
No API Key	1 req/sec	Default
Free API Key	1 req/sec	Sign up
Academic Partner	10-100 req/sec	Apply via S2

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

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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.

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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/

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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.

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Related MCP servers by the same author

• alphafold-sovereign-mcp — Model Context Protocol server for AlphaFold DB and 13 other biomedical data sources, with a local SQLite knowledge graph (pip install --pre alphafold-sovereign-mcp).
• uniprot-mcp — Model Context Protocol server for UniProt Swiss-Prot and TrEMBL (pip install uniprot-mcp-server).

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License

MIT License - see LICENSE file.

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Author

Santiago Maniches

• Founder & CEO, TOPOLOGICA LLC
• ORCID: 0009-0005-6480-1987
• LinkedIn: santiago-maniches
• Website: topologica.ai

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Contributing

Contributions welcome! Please read our Contributing Guidelines.

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Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Contact: [e-mail]

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Advancing computational research through topological intelligence