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

```python