Paper Search MCP Server

Instructions

USE THIS TOOL WHEN:
- Searching for PREPRINTS (not peer-reviewed yet)
- You need free, immediate access to full-text PDFs
- Searching in: Physics, Mathematics, Computer Science, Statistics,
  Quantitative Biology, Quantitative Finance, Electrical Engineering

NOTE: arXiv is a PREPRINT server - papers may not be peer-reviewed.
For peer-reviewed papers, use search_crossref or search_semantic.

WORKFLOW:
1. search_arxiv(query) -> get paper_id (e.g., '2106.12345')
2. download_arxiv(paper_id) -> get PDF (always available)
3. read_arxiv_paper(paper_id) -> get full text as Markdown

Args:
    query: Search terms in any supported field.
    max_results: Number of results (default: 10).

Returns:
    List of paper dicts with: paper_id, title, authors, abstract, 
    published_date, pdf_url, categories.

Example:
    search_arxiv("quantum computing error correction", max_results=5)

Implementation Reference

• paper_find_mcp/server.py:162-192 (handler)

  Primary handler for the search_arxiv MCP tool. Registered with @mcp.tool() decorator. Delegates to shared _search helper using 'arxiv' searcher instance.

  @mcp.tool()
  async def search_arxiv(query: str, max_results: int = 10) -> List[Dict]:
      """Search preprints on arXiv - major open-access preprint server.
      
      USE THIS TOOL WHEN:
      - Searching for PREPRINTS (not peer-reviewed yet)
      - You need free, immediate access to full-text PDFs
      - Searching in: Physics, Mathematics, Computer Science, Statistics,
        Quantitative Biology, Quantitative Finance, Electrical Engineering
      
      NOTE: arXiv is a PREPRINT server - papers may not be peer-reviewed.
      For peer-reviewed papers, use search_crossref or search_semantic.
      
      WORKFLOW:
      1. search_arxiv(query) -> get paper_id (e.g., '2106.12345')
      2. download_arxiv(paper_id) -> get PDF (always available)
      3. read_arxiv_paper(paper_id) -> get full text as Markdown
  
      Args:
          query: Search terms in any supported field.
          max_results: Number of results (default: 10).
      
      Returns:
          List of paper dicts with: paper_id, title, authors, abstract, 
          published_date, pdf_url, categories.
      
      Example:
          search_arxiv("quantum computing error correction", max_results=5)
      """
      return await _search('arxiv', query, max_results)

• paper_find_mcp/server.py:95-113 (helper)

  Shared helper function _search that all platform tools (including search_arxiv) call to execute searches via platform-specific searcher instances.

  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 (registration)

  Global SEARCHERS dictionary registering the ArxivSearcher instance under 'arxiv' key, used by _search helper.

  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/arxiv.py:60-115 (handler)

  Core search logic in ArxivSearcher.search(): queries arXiv API (export.arxiv.org/api/query), parses Atom feed with feedparser, constructs Paper objects from entries.

  def search(self, query: str, max_results: int = 10) -> List[Paper]:
      """搜索 arXiv 论文
      
      Args:
          query: 搜索关键词，支持 arXiv 查询语法
                 例如: "ti:attention" (标题), "au:hinton" (作者)
          max_results: 最大返回数量
          
      Returns:
          List[Paper]: 论文列表
      """
      params = {
          'search_query': query,
          'max_results': max_results,
          'sortBy': 'submittedDate',
          'sortOrder': 'descending'
      }
      
      try:
          response = requests.get(self.BASE_URL, params=params, timeout=30)
          response.raise_for_status()
      except requests.RequestException as e:
          logger.error(f"arXiv API request failed: {e}")
          return []
      
      feed = feedparser.parse(response.content)
      papers = []
      
      for entry in feed.entries:
          try:
              authors = [author.name for author in entry.authors]
              published = datetime.strptime(entry.published, '%Y-%m-%dT%H:%M:%SZ')
              updated = datetime.strptime(entry.updated, '%Y-%m-%dT%H:%M:%SZ')
              pdf_url = next(
                  (link.href for link in entry.links if link.type == 'application/pdf'), 
                  ''
              )
              papers.append(Paper(
                  paper_id=entry.id.split('/')[-1],
                  title=entry.title.replace('\n', ' ').strip(),
                  authors=authors,
                  abstract=entry.summary.replace('\n', ' ').strip(),
                  url=entry.id,
                  pdf_url=pdf_url,
                  published_date=published,
                  updated_date=updated,
                  source='arxiv',
                  categories=[tag.term for tag in entry.tags],
                  keywords=[],
                  doi=entry.get('doi', '')
              ))
          except Exception as e:
              logger.warning(f"Error parsing arXiv entry: {e}")
              
      return papers

• paper_find_mcp/academic_platforms/arxiv.py:24-35 (schema)

  Abstract base class PaperSource defining the interface/schema for all searchers, including ArxivSearcher with search method signature.

  class PaperSource:
      """Abstract base class for paper sources"""
      def search(self, query: str, **kwargs) -> List[Paper]:
          raise NotImplementedError
  
      def download_pdf(self, paper_id: str, save_path: str) -> str:
          raise NotImplementedError
  
      def read_paper(self, paper_id: str, save_path: str) -> str:
          raise NotImplementedError