Semantic Scholar MCP Server
by YUZongmin
Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.
Schema
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
No tools | |
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| SEMANTIC_SCHOLAR_API_KEY | No | Your Semantic Scholar API key. If not provided, the server will use unauthenticated access with lower rate limits. |
README.md
Semantic Scholar MCP Server
A FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.
Features
- Paper Search & Discovery
- Full-text search with advanced filtering
- Title-based paper matching
- Paper recommendations (single and multi-paper)
- Batch paper details retrieval
- Advanced search with ranking strategies
- Citation Analysis
- Citation network exploration
- Reference tracking
- Citation context and influence analysis
- Author Information
- Author search and profile details
- Publication history
- Batch author details retrieval
- Advanced Features
- Complex search with multiple ranking strategies
- Customizable field selection
- Efficient batch operations
- Rate limiting compliance
- Support for both authenticated and unauthenticated access
- Graceful shutdown and error handling
- Connection pooling and resource management
System Requirements
- Python 3.8+
- FastMCP framework
- Environment variable for API key (optional)
Installation
Install using FastMCP:
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fastmcp install semantic-scholar-server.py --name "Semantic Scholar" -e SEMANTIC_SCHOLAR_API_KEY=your-api-key
The -e SEMANTIC_SCHOLAR_API_KEY parameter is optional. If not provided, the server will use unauthenticated access with lower rate limits.
Configuration
Environment Variables
SEMANTIC_SCHOLAR_API_KEY: Your Semantic Scholar API key (optional)- Get your key from Semantic Scholar API
- If not provided, the server will use unauthenticated access
Rate Limits
The server automatically adjusts to the appropriate rate limits:
With API Key:
- Search, batch and recommendation endpoints: 1 request per second
- Other endpoints: 10 requests per second
Without API Key:
- All endpoints: 100 requests per 5 minutes
- Longer timeouts for requests
Available MCP Tools
Note: All tools are aligned with the official Semantic Scholar API documentation. Please refer to the official documentation for detailed field specifications and the latest updates.
Paper Search Tools
paper_relevance_search: Search for papers using relevance ranking- Supports comprehensive query parameters including year range and citation count filters
- Returns paginated results with customizable fields
paper_bulk_search: Bulk paper search with sorting options- Similar to relevance search but optimized for larger result sets
- Supports sorting by citation count, publication date, etc.
paper_title_search: Find papers by exact title match- Useful for finding specific papers when you know the title
- Returns detailed paper information with customizable fields
paper_details: Get comprehensive details about a specific paper- Accepts various paper ID formats (S2 ID, DOI, ArXiv, etc.)
- Returns detailed paper metadata with nested field support
paper_batch_details: Efficiently retrieve details for multiple papers- Accepts up to 1000 paper IDs per request
- Supports the same ID formats and fields as single paper details
Citation Tools
paper_citations: Get papers that cite a specific paper- Returns paginated list of citing papers
- Includes citation context when available
- Supports field customization and sorting
paper_references: Get papers referenced by a specific paper- Returns paginated list of referenced papers
- Includes reference context when available
- Supports field customization and sorting
Author Tools
author_search: Search for authors by name- Returns paginated results with customizable fields
- Includes affiliations and publication counts
author_details: Get detailed information about an author- Returns comprehensive author metadata
- Includes metrics like h-index and citation counts
author_papers: Get papers written by an author- Returns paginated list of author's publications
- Supports field customization and sorting
author_batch_details: Get details for multiple authors- Efficiently retrieve information for up to 1000 authors
- Returns the same fields as single author details
Recommendation Tools
paper_recommendations_single: Get recommendations based on a single paper- Returns similar papers based on content and citation patterns
- Supports field customization for recommended papers
paper_recommendations_multi: Get recommendations based on multiple papers- Accepts positive and negative example papers
- Returns papers similar to positive examples and dissimilar to negative ones
Usage Examples
Basic Paper Search
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results = await paper_relevance_search(
context,
query="machine learning",
year="2020-2024",
min_citation_count=50,
fields=["title", "abstract", "authors"]
)
Paper Recommendations
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# Single paper recommendation
recommendations = await paper_recommendations_single(
context,
paper_id="649def34f8be52c8b66281af98ae884c09aef38b",
fields="title,authors,year"
)
# Multi-paper recommendation
recommendations = await paper_recommendations_multi(
context,
positive_paper_ids=["649def34f8be52c8b66281af98ae884c09aef38b", "ARXIV:2106.15928"],
negative_paper_ids=["ArXiv:1805.02262"],
fields="title,abstract,authors"
)
Batch Operations
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# Get details for multiple papers
papers = await paper_batch_details(
context,
paper_ids=["649def34f8be52c8b66281af98ae884c09aef38b", "ARXIV:2106.15928"],
fields="title,authors,year,citations"
)
# Get details for multiple authors
authors = await author_batch_details(
context,
author_ids=["1741101", "1780531"],
fields="name,hIndex,citationCount,paperCount"
)
Error Handling
The server provides standardized error responses:
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{
"error": {
"type": "error_type", # rate_limit, api_error, validation, timeout
"message": "Error description",
"details": {
# Additional context
"authenticated": true/false # Indicates if request was authenticated
}
}
}
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