Paper Search MCP Server

Instructions

Implementation Reference

• paper_find_mcp/server.py:624-657 (handler)

  Main handler function for the search_crossref tool. Registered via @mcp.tool() decorator. Includes schema via type hints and docstring. Delegates to _search helper using 'crossref' searcher.
  @mcp.tool() async def search_crossref( query: str, max_results: int = 10, **kwargs ) -> List[Dict]: """Search academic papers in CrossRef - the largest DOI citation database. USE THIS TOOL WHEN: - You need to find papers by DOI or citation metadata - You want to search across all academic publishers (not just preprints) - You need publication metadata like journal, volume, issue, citations - You want to verify if a DOI exists or get its metadata CrossRef indexes 150M+ scholarly works from thousands of publishers. Results include DOI, authors, title, abstract, citations, and publisher info. Args: query: Search terms (e.g., 'machine learning', 'CRISPR gene editing'). max_results: Number of results (default: 10, max: 1000). **kwargs: Optional filters: - filter: 'has-full-text:true,from-pub-date:2020' - sort: 'relevance' | 'published' | 'cited' - order: 'asc' | 'desc' Returns: List of paper metadata dicts with keys: paper_id (DOI), title, authors, abstract, doi, published_date, citations, url. Example: search_crossref("attention mechanism transformer", max_results=5) """ return await _search('crossref', query, max_results, **kwargs)
• paper_find_mcp/server.py:75-85 (registration)

  Global SEARCHERS dictionary registering the CrossRefSearcher instance under 'crossref' key, used by the generic _search function.
  SEARCHERS = { 'arxiv': ArxivSearcher(), 'pubmed': PubMedSearcher(), 'biorxiv': BioRxivSearcher(), 'medrxiv': MedRxivSearcher(), 'google_scholar': GoogleScholarSearcher(), 'iacr': IACRSearcher(), 'semantic': SemanticSearcher(), 'crossref': CrossRefSearcher(), 'repec': RePECSearcher(), }
• paper_find_mcp/server.py:95-113 (helper)

  Generic _search helper function that retrieves the searcher instance and calls its search method, converting results to dicts.
  async def _search( searcher_name: str, query: str, max_results: int = 10, **kwargs ) -> List[Dict]: """通用搜索函数""" searcher = SEARCHERS.get(searcher_name) if not searcher: logger.error(f"Unknown searcher: {searcher_name}") return [] try: papers = searcher.search(query, max_results=max_results, **kwargs) return [paper.to_dict() for paper in papers] except Exception as e: logger.error(f"Search failed for {searcher_name}: {e}") return []
• paper_find_mcp/academic_platforms/crossref.py:109-168 (helper)

  Core search implementation in CrossRefSearcher class: makes API request to CrossRef /works endpoint, handles retries, parses responses into Paper objects.
  def search(self, query: str, max_results: int = 10, **kwargs) -> List[Paper]: """ Search CrossRef database for papers. Args: query: Search query string max_results: Maximum number of results to return (default: 10) **kwargs: Additional parameters like filters, sort, etc. Returns: List of Paper objects """ try: params = { 'query': query, 'rows': min(max_results, 1000), # CrossRef API max is 1000 'sort': 'relevance', 'order': 'desc' } # Add any additional filters from kwargs if 'filter' in kwargs: params['filter'] = kwargs['filter'] if 'sort' in kwargs: params['sort'] = kwargs['sort'] if 'order' in kwargs: params['order'] = kwargs['order'] # Polite Pool 参数 if self.mailto: params['mailto'] = self.mailto url = f"{self.BASE_URL}/works" response = self._make_request(url, params) if not response: return [] data = response.json() papers = [] items = data.get('message', {}).get('items', []) for item in items: try: paper = self._parse_crossref_item(item) if paper: papers.append(paper) except Exception as e: logger.warning(f"Error parsing CrossRef item: {e}") continue return papers except requests.RequestException as e: logger.error(f"Error searching CrossRef: {e}") return [] except Exception as e: logger.error(f"Unexpected error in CrossRef search: {e}") return []
• paper_find_mcp/server.py:34-79 (helper)

  Import of CrossRefSearcher class used by the server.
  from .academic_platforms.crossref import CrossRefSearcher from .academic_platforms.repec import RePECSearcher from .academic_platforms.sci_hub import SciHubFetcher, check_paper_year from .paper import Paper # ============================================================ # 配置 # ============================================================ # PDF 下载目录，可通过环境变量 PAPER_DOWNLOAD_PATH 配置 # 默认为用户目录下的 paper_downloads (跨平台兼容) # - macOS: ~/paper_downloads # - Linux: ~/paper_downloads # - Windows: C:\Users\<username>\paper_downloads from pathlib import Path def get_download_path() -> str: """获取下载路径，支持跨平台 注意：此函数每次调用时都会重新计算路径，以确保： 1. 环境变量 PAPER_DOWNLOAD_PATH 的变化能够生效 2. MCP 在不同环境下运行时能正确获取 HOME 目录 """ env_path = os.environ.get("PAPER_DOWNLOAD_PATH") if env_path: return env_path # 使用 Path.home() 获取跨平台的用户主目录 return str(Path.home() / "paper_downloads") # ============================================================ # 日志配置 # ============================================================ logger = logging.getLogger(__name__) # ============================================================ # MCP Server 初始化 # ============================================================ mcp = FastMCP("paper_search_server") # ============================================================ # 搜索器实例（单例） # ============================================================ SEARCHERS = { 'arxiv': ArxivSearcher(), 'pubmed': PubMedSearcher(), 'biorxiv': BioRxivSearcher(), 'medrxiv': MedRxivSearcher(),