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

Installing via Smithery

To install Semantic Scholar MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install semantic-scholar-fastmcp-mcp-server --client claude

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
        }
    }
}

Integrations