Paper Search MCP Server

Instructions

Implementation Reference

• paper_find_mcp/server.py:547-581 (handler)

  Primary handler and registration for the 'search_semantic' tool. Defines input schema via type hints and docstring. Delegates to _search('semantic', ...) using the SemanticSearcher instance.
  @mcp.tool() async def search_semantic( query: str, year: Optional[str] = None, max_results: int = 10 ) -> List[Dict]: """Search papers on Semantic Scholar - general-purpose academic search engine. USE THIS TOOL WHEN: - You want to search across ALL academic disciplines - You need citation counts and influence metrics - You want to filter by publication year - You need open-access PDF links when available COVERAGE: ALL academic fields - sciences, humanities, medicine, etc. Indexes 200M+ papers from journals, conferences, and preprints. WORKFLOW: 1. search_semantic(query) -> get paper_id or DOI 2. download_semantic(paper_id) -> get PDF (if open-access) 3. If no PDF: use download_scihub(doi) for older papers Args: query: Search terms (any topic, any field). year: Optional year filter: '2023', '2020-2023', '2020-', '-2019'. max_results: Number of results (default: 10). Returns: List of paper dicts with: paper_id, title, authors, abstract, published_date, doi, citations, url, pdf_url (if available). Example: search_semantic("climate change impact agriculture", year="2020-", max_results=5) """ kwargs = {'year': year} if year else {} return await _search('semantic', query, max_results, **kwargs)
• paper_find_mcp/server.py:95-113 (helper)

  Generic helper function used by search_semantic (and other tools) to invoke the searcher.search() method and convert 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/server.py:75-85 (helper)

  Global SEARCHERS dictionary that instantiates SemanticSearcher() for the 'semantic' key, used by _search.
  SEARCHERS = { 'arxiv': ArxivSearcher(), 'pubmed': PubMedSearcher(), 'biorxiv': BioRxivSearcher(), 'medrxiv': MedRxivSearcher(), 'google_scholar': GoogleScholarSearcher(), 'iacr': IACRSearcher(), 'semantic': SemanticSearcher(), 'crossref': CrossRefSearcher(), 'repec': RePECSearcher(), }
• paper_find_mcp/academic_platforms/semantic.py:245-289 (helper)

  Core implementation of semantic search logic in SemanticSearcher.search(): queries Semantic Scholar API, handles rate limiting, parses results into Paper objects.
  def search( self, query: str, year: Optional[str] = None, max_results: int = 10 ) -> List[Paper]: """搜索论文 Args: query: 搜索关键词 year: 年份过滤（支持格式："2019", "2016-2020", "2010-", "-2015"） max_results: 最大返回数量 Returns: List[Paper]: 论文列表 """ params = { "query": query, "limit": min(max_results, 100), # API 限制 "fields": ",".join(self.DEFAULT_FIELDS), } if year: params["year"] = year response = self._make_request("paper/search", params) if not response: return [] try: data = response.json() results = data.get('data', []) except Exception as e: logger.error(f"Failed to parse response: {e}") return [] papers = [] for item in results[:max_results]: paper = self._parse_paper(item) if paper: papers.append(paper) logger.info(f"Found {len(papers)} papers for query: {query}") return papers