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A FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.

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Semantic Scholar MCP Server

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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
        }
    }
}

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A FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.

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