Semantic Scholar MCP Server

# Semantic Scholar MCP Server [](https://smithery.ai/server/semantic-scholar-fastmcp-mcp-server) A FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks. ## Project Structure The project has been refactored into a modular structure for better maintainability: ``` semantic-scholar-server/ ├── semantic_scholar/ # Main package │ ├── __init__.py # Package initialization │ ├── server.py # Server setup and main functionality │ ├── mcp.py # Centralized FastMCP instance definition │ ├── config.py # Configuration classes │ ├── utils/ # Utility modules │ │ ├── __init__.py │ │ ├── errors.py # Error handling │ │ └── http.py # HTTP client and rate limiting │ ├── api/ # API endpoints │ ├── __init__.py │ ├── papers.py # Paper-related endpoints │ ├── authors.py # Author-related endpoints │ └── recommendations.py # Recommendation endpoints ├── run.py # Entry point script ``` This structure: - Separates concerns into logical modules - Makes the codebase easier to understand and maintain - Allows for better testing and future extensions - Keeps related functionality grouped together - Centralizes the FastMCP instance to avoid circular imports ## 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](https://smithery.ai/server/semantic-scholar-fastmcp-mcp-server): ```bash npx -y @smithery/cli install semantic-scholar-fastmcp-mcp-server --client claude ``` ### Manual Installation 1. Clone the repository: ```bash git clone https://github.com/YUZongmin/semantic-scholar-fastmcp-mcp-server.git cd semantic-scholar-server ``` 2. Install FastMCP and other dependencies following: https://github.com/jlowin/fastmcp 3. Configure FastMCP: For Claude Desktop users, you'll need to configure the server in your FastMCP configuration file. Add the following to your configuration (typically in `~/.config/claude-desktop/config.json`): ```json { "mcps": { "Semantic Scholar Server": { "command": "/path/to/your/venv/bin/fastmcp", "args": [ "run", "/path/to/your/semantic-scholar-server/run.py" ], "env": { "SEMANTIC_SCHOLAR_API_KEY": "your-api-key-here" # Optional } } } } ``` Make sure to: - Replace `/path/to/your/venv/bin/fastmcp` with the actual path to your FastMCP installation - Replace `/path/to/your/semantic-scholar-server/run.py` with the actual path to run.py on your machine - If you have a Semantic Scholar API key, add it to the `env` section. If not, you can remove the `env` section entirely 4. Start using the server: The server will now be available to your Claude Desktop instance. No need to manually run any commands - Claude will automatically start and manage the server process when needed. ### API Key (Optional) To get higher rate limits and better performance: 1. Get an API key from [Semantic Scholar API](https://www.semanticscholar.org/product/api) 2. Add it to your FastMCP configuration as shown above in the `env` section If no API key is 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](https://www.semanticscholar.org/product/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](https://api.semanticscholar.org/api-docs/). 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 ```python results = await paper_relevance_search( context, query="machine learning", year="2020-2024", min_citation_count=50, fields=["title", "abstract", "authors"] ) ``` ### Paper Recommendations ```python # 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 ```python # 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: ```python { "error": { "type": "error_type", # rate_limit, api_error, validation, timeout "message": "Error description", "details": { # Additional context "authenticated": true/false # Indicates if request was authenticated } } } ```