Semantic Scholar MCP Server: Lightning-fast Access & Smarter Search

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:

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

results = await paper_relevance_search(
    context,
    query="machine learning",
    year="2020-2024",
    min_citation_count=50,
    fields=["title", "abstract", "authors"]
)

Paper Recommendations

# 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

# 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:

{
    "error": {
        "type": "error_type",  # rate_limit, api_error, validation, timeout
        "message": "Error description",
        "details": {
            # Additional context
            "authenticated": true/false  # Indicates if request was authenticated
        }
    }
}