academic-literature-search

name academic-literature-search description null Academic Literature Search — 学术文献检索与引用管理 Use this skill when the user asks to search for academic papers, retrieve literature, generate citations, format references, or any task involving PubMed, bioRxiv, arXiv, or academic reference management. Trigger keywords: "搜文献", "检索", "找论文", "参考文献", "引用", "citation", "search papers", "PubMed", "bioRxiv", "arXiv", "GB/T 7714", "PMID", "DOI", "批量引用". Core Principles MCP first, Python second : PubMed operations → MCP tools (zero code). Python only for arXiv, GB/T 7714 formatting, and citation post-processing. Code-driven citations : Citation formatting, validation, deduplication, renumbering — ALL via Python code. NEVER fabricate PMIDs, DOIs, author names, or journal names. GB/T 7714-2015 sequential numbering : [1][2][3] in-text, references numbered by order of first appearance. Journal name consistency : Use full journal names throughout (NOT ISO abbreviations). E.g., Nature Medicine not Nat Med . If MCP returns abbreviated names, expand them; if expansion is uncertain, use the name as returned. Tool Routing Decision Table 操作 用什么 为什么 PubMed 关键词搜索 MCP pubmed_search_articles 原生日期/类型过滤/排序，Agent 零代码 PMID 批量获取详情 MCP pubmed_fetch_contents 4种详情级别，一次200个，含 MeSH 相似论文发现 MCP pubmed_article_connections (similar) 直接调用，返回结构化数据 被引论文发现 MCP pubmed_article_connections (citedin) 同上 论文参考文献（它引了谁） MCP pubmed_article_connections (references) MCP 独有 ，ELink 不支持 RIS/BibTeX 导出 MCP pubmed_article_connections (citation_formats) 内置格式化 bioRxiv/medRxiv 搜索 MCP pubmed_search_articles + journal filter queryTerm 加 biorxiv[journal] arXiv 搜索 Python MCP 不覆盖 arXiv GB/T 7714-2015 格式化 Python MCP 无国标格式 引用后处理/去重/编号 Python MCP 不覆盖 MCP Operations (PubMed — 主力) 1. 关键词搜索 Tool: pubmed_search_articles Parameters: queryTerm: "large language model bioinformatics" maxResults: 20 sortBy: "relevance" ← 或 "pub_date" fetchBriefSummaries: 10 ← 返回前10篇摘要 dateRange: ← 可选 minDate: "2022" maxDate: "2026" dateType: "pdat" filterByPublicationTypes: ["Review"] ← 可选 bioRxiv/medRxiv ：queryTerm 加 journal filter： queryTerm: "(large language model agent) AND (biorxiv[journal] OR medrxiv[journal])" 2. PMID 批量获取详情 Tool: pubmed_fetch_contents Parameters: pmids: ["39361263", "38768397", "36869294"] detailLevel: "abstract_plus" ← 推荐，解析后的结构化数据 includeMeshTerms: true 详情级别： abstract_plus （推荐）| citation_data （轻量）| full_xml | medline_text 3. 相关论文发现 Tool: pubmed_article_connections Parameters: sourcePmid: "39361263" relationshipType: "pubmed_similar_articles" ← 或 citedin / references / citation_formats maxRelatedResults: 15 4. 引用格式导出 Tool: pubmed_article_connections Parameters: sourcePmid: "39361263" relationshipType: "citation_formats" citationStyles: ["ris", "bibtex", "apa_string"] Python Operations arXiv 搜索 Uses stdlib only ( urllib.request + xml.etree.ElementTree ), no external dependencies: import urllib.request, urllib.parse, xml.etree.ElementTree as ET, re def _parse_author_name ( full_name ): """Convert 'Shunyu Yao' → 'Yao S' (GB/T 7714: surname first, initials).""" parts = full_name.strip().split() if not parts: return full_name if len (parts) == 1 : return parts[ 0 ] surname = parts[- 1 ] initials = "" .join(p[ 0 ].upper() for p in parts[:- 1 ]) return f" {surname} {initials} " def search_arxiv ( query, max_results= 10 ): """Search arXiv. Free API, no key needed. Uses only stdlib.""" params = urllib.parse.urlencode({ "search_query" : f"all: {query} " , "start" : 0 , "max_results" : max_results, "sortBy" : "relevance" , "sortOrder" : "descending" }) url = f"https://export.arxiv.org/api/query? {params} " with urllib.request.urlopen(url, timeout= 30 ) as resp: xml_text = resp.read().decode( "utf-8" ) ns = { "atom" : "http://www.w3.org/2005/Atom" , "arxiv" : "http://arxiv.org/schemas/atom" } root = ET.fromstring(xml_text) articles = [] for entry in root.findall( "atom:entry" , ns): title = (entry.findtext( "atom:title" , "" , ns) or "" ).replace( "\n" , " " ).strip() abstract = (entry.findtext( "atom:summary" , "" , ns) or "" ).replace( "\n" , " " ).strip() raw_authors = [a.findtext( "atom:name" , "" , ns).strip() for a in entry.findall( "atom:author" , ns)] authors_gbt = [_parse_author_name(a) for a in raw_authors] published = entry.findtext( "atom:published" , "" , ns)[: 10 ] id_url = entry.findtext( "atom:id" , "" , ns) or "" arxiv_id = id_url.split( "/abs/" )[- 1 ] if "/abs/" in id_url else "" doi_e = entry.find( "arxiv:doi" , ns) doi = doi_e.text.strip() if doi_e is not None and doi_e.text else "" cat_e = entry.find( "arxiv:primary_category" , ns) cat = cat_e.get( "term" , "" ) if cat_e is not None else "" articles.append({ "source" : "arxiv" , "pmid" : "" , "doi" : doi, "arxiv_id" : arxiv_id, "title" : title, "authors" : authors_gbt, "journal" : f"arXiv: {arxiv_id} " , "year" : published[: 4 ] if published else "" , "volume" : "" , "issue" : "" , "pages" : "" , "abstract" : abstract, "url" : f"https://arxiv.org/abs/ {arxiv_id} " , "category" : cat, "venue" : "" , # populated manually for published conference papers }) return articles GB/T 7714-2015 格式化 (完整版) 支持 [J] 期刊、[Z/OL] 预印本、[C] 会议论文、[M] 专著、[D] 学位论文。 所有字段均做 None 安全处理。 def format_gbt7714 ( article, seq_num ): """Format one article as GB/T 7714-2015 sequential reference. Handles: MCP article dicts (authors as list of dicts, journalInfo as nested dict), Python-parsed dicts (flat fields), and arXiv dicts. Supports: [J] journal, [Z/OL] preprint, [C] conference, [M] book, [D] dissertation. """ # ── Author parsing (handles 4 input formats) ── authors_raw = article.get( "authors" ) or [] if isinstance (authors_raw, str ): authors = [a.strip() for a in authors_raw.split( "," ) if a.strip()] elif authors_raw and isinstance (authors_raw[ 0 ], dict ): authors = [] for a in authors_raw: last = a.get( "lastName" ) or "" ini = a.get( "initials" ) or (a.get( "firstName" ) or "" )[: 1 ] if last: authors.append( f" {last} {ini} " .strip()) else : authors = [ str (a) for a in authors_raw if a] if len (authors) > 3 : author_str = ", " .join(authors[: 3 ]) + ", et al" elif authors: author_str = ", " .join(authors) else : author_str = "Anonymous" title = (article.get( "title" ) or "Untitled" ).rstrip( "." ) source = article.get( "source" ) or "pubmed" doi = (article.get( "doi" ) or "" ).strip() # ── Journal info (MCP nested dict vs flat fields) ── journal_info = article.get( "journalInfo" ) if journal_info and isinstance (journal_info, dict ): journal = journal_info.get( "title" ) or journal_info.get( "isoAbbreviation" ) or "" volume = journal_info.get( "volume" ) or "" issue = journal_info.get( "issue" ) or "" pages = journal_info.get( "pages" ) or "" pub_date = journal_info.get( "publicationDate" ) or {} year = pub_date.get( "year" , "" ) if isinstance (pub_date, dict ) else str (pub_date)[: 4 ] else : journal = article.get( "journal" ) or "" volume = article.get( "volume" ) or "" issue = article.get( "issue" ) or "" pages = article.get( "pages" ) or "" year = article.get( "year" ) or "" venue = article.get( "venue" ) or "" # for conference papers # ── Detect document type ── doc_type = article.get( "doc_type" ) or "" # explicit override if not doc_type: jl = (journal or "" ).lower() if source in ( "biorxiv" , "medrxiv" ) or "biorxiv" in jl or "medrxiv" in jl: doc_type = "preprint" elif source == "arxiv" : if venue: doc_type = "conference" else : doc_type = "preprint" elif venue: doc_type = "conference" else : doc_type = "journal" # ── Format by document type ── if doc_type == "preprint" : platform = "bioRxiv" if "biorxiv" in (journal or "" ).lower() else \ "medRxiv" if "medrxiv" in (journal or "" ).lower() else \ f"arXiv: {article.get( 'arxiv_id' , '' )} " if source == "arxiv" else \ journal or "Preprint" ref = f"[ {seq_num} ] {author_str} . {title} [Z/OL]. {platform} , {year} " if doi: ref += f". DOI: {doi} " ref += "." elif doc_type == "conference" : # [C]//Conference Name. City: Publisher, Year: Pages. # Simplified: if venue is known, use it; otherwise fall back ref = f"[ {seq_num} ] {author_str} . {title} [C]// {venue} " if year: ref += f", {year} " if pages: ref += f": {pages} " ref += "." if doi: ref += f" DOI: {doi} ." elif doc_type == "book" : publisher = article.get( "publisher" ) or "" city = article.get( "city" ) or "" ref = f"[ {seq_num} ] {author_str} . {title} [M]" if city or publisher: ref += f". {city} : {publisher} " if city else f". {publisher} " if year: ref += f", {year} " ref += "." elif doc_type == "dissertation" : institution = article.get( "institution" ) or "" city = article.get( "city" ) or "" ref = f"[ {seq_num} ] {author_str} . {title} [D]" if city or institution: ref += f". {city} : {institution} " if city else f". {institution} " if year: ref += f", {year} " ref += "." else : # Default: [J] journal article ref = f"[ {seq_num} ] {author_str} . {title} [J]. {journal} , {year} "