PubMed-MCP-Server

import xml.etree.ElementTree as ET from urllib.parse import quote import os from collections import Counter import re import requests def generate_pubmed_search_url(term=None, title=None, author=None, journal=None, start_date=None, end_date=None, num_results=10): """根据用户输入的字段生成 PubMed 搜索 URL""" base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" query_parts = [] if term: query_parts.append(quote(term)) if title: query_parts.append(f"{quote(title)}[Title]") if author: query_parts.append(f"{quote(author)}[Author]") if journal: query_parts.append(f"{quote(journal)}[Journal]") if start_date and end_date: query_parts.append(f"{start_date}:{end_date}[Date - Publication]") query = " AND ".join(query_parts) params = { "db": "pubmed", "term": query, "retmax": num_results, "retmode": "xml" } return f"{base_url}?{'&'.join([f'{k}={v}' for k, v in params.items()])}" def search_pubmed(search_url): """从 PubMed 搜索结果中解析文章 ID""" response = requests.get(search_url) if response.status_code == 200: root = ET.fromstring(response.content) id_list = root.find("IdList") if id_list is not None: return [id.text for id in id_list.findall("Id")] else: print("No results found.") return [] else: print(f"Error: Unable to fetch data (status code: {response.status_code})") return [] def get_pubmed_metadata(pmid): """使用 PubMed API 通过 PMID 获取文章的详细元数据""" url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id={pmid}&retmode=xml" response = requests.get(url) if response.status_code == 200: root = ET.fromstring(response.content) article = root.find(".//Article") if article is not None: title = article.find(".//ArticleTitle") title = title.text if title is not None else "No title available" abstract = article.find(".//Abstract/AbstractText") abstract = abstract.text if abstract is not None else "No abstract available" authors = [] for author in article.findall(".//Author"): last_name = author.find(".//LastName") if last_name is not None and last_name.text: authors.append(last_name.text) authors = ", ".join(authors) if authors else "No authors available" journal = article.find(".//Journal/Title") journal = journal.text if journal is not None else "No journal available" pub_date = article.find(".//PubDate/Year") pub_date = pub_date.text if pub_date is not None else "No publication date available" return { "PMID": pmid, "Title": title, "Authors": authors, "Journal": journal, "Publication Date": pub_date, "Abstract": abstract } else: print(f"No article data found for PMID: {pmid}") return None else: print(f"Error: Unable to fetch metadata (status code: {response.status_code})") return None def download_full_text_pdf(pmid): """尝试下载全文 PDF 或提供文章链接""" print(f"Attempting to access full text for PMID: {pmid}") # 首先，我们需要检查这篇文章是否有PMC ID efetch_url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id={pmid}&retmode=xml" headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36' } response = requests.get(efetch_url, headers=headers) if response.status_code != 200: print(f"Error: Unable to fetch article data (status code: {response.status_code})") return f"Error: Unable to fetch article data (status code: {response.status_code})" root = ET.fromstring(response.content) pmc_id = root.find(".//ArticleId[@IdType='pmc']") if pmc_id is None: print(f"No PMC ID found for PMID: {pmid}") pubmed_url = f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" print(f"You can check the article availability at: {pubmed_url}") return f"No PMC ID found for PMID: {pmid}" + "\n" + f"You can check the article availability at: {pubmed_url}" pmc_id = pmc_id.text # 检查文章是否为开放访问 pmc_url = f"https://www.ncbi.nlm.nih.gov/pmc/articles/{pmc_id}/" pmc_response = requests.get(pmc_url, headers=headers) if pmc_response.status_code != 200: print(f"Error: Unable to access PMC article page (status code: {pmc_response.status_code})") print(f"You can check the article availability at: {pmc_url}") return f"Error: Unable to access PMC article page (status code: {pmc_response.status_code})" + "\n" + f"You can check the article availability at: {pmc_url}"f"You can check the article availability at: {pmc_url}" if "This article is available under a" not in pmc_response.text: print(f"The article doesn't seem to be fully open access.") print(f"You can check the article availability at: {pmc_url}") return f"The article doesn't seem to be fully open access." + "\n" + f"You can check the article availability at: {pmc_url}" # 尝试下载PDF pdf_url = f"https://www.ncbi.nlm.nih.gov/pmc/articles/{pmc_id}/pdf" pdf_response = requests.get(pdf_url, headers=headers) if pdf_response.status_code != 200: print(f"Error: Unable to download PDF (status code: {pdf_response.status_code})") print(f"You can try accessing the article directly at: {pmc_url}") return f"Error: Unable to download PDF (status code: {pdf_response.status_code})" + "\n" + f"You can try accessing the article directly at: {pmc_url}" # 保存PDF文件 filename = f"PMID_{pmid}_PMC_{pmc_id}.pdf" with open(filename, 'wb') as f: f.write(pdf_response.content) print(f"PDF for PMID {pmid} has been downloaded as {filename}") return f"PDF for PMID {pmid} has been downloaded as {filename}" def deep_paper_analysis(paper_metadata): """ Generate a prompt for deep paper analysis Parameters: paper_metadata (dict): A dictionary containing paper metadata Returns: str: A prompt for deep analysis """ title = paper_metadata['Title'] authors = paper_metadata['Authors'] journal = paper_metadata['Journal'] pub_date = paper_metadata['Publication Date'] abstract = paper_metadata['Abstract'] prompt = f""" As an expert in scientific paper analysis, please provide a comprehensive analysis of the following paper: Title: {title} Authors: {authors} Journal: {journal} Publication Date: {pub_date} Abstract: {abstract} Please address the following aspects in your analysis: 1. Research Background and Significance: 2. Main Research Questions or Hypotheses: 3. Methodology Overview: 4. Key Findings and Results: 5. Conclusions and Implications: 6. Limitations of the Study: 7. Future Research Directions: 8. Relationship to Other Studies in the Field: 9. Overall Evaluation of the Research: Ensure your analysis is thorough, objective, and based on the information provided in the paper. If certain information is missing from the abstract, please note this and provide possible inferences or suggestions based on your expertise. """ return prompt def search_key_words(key_words, num_results=10): # 生成搜索 URL search_url = generate_pubmed_search_url(term=key_words, num_results=num_results) print("Generated URL:", search_url) # 获取并解析搜索结果 pmids = search_pubmed(search_url) articles = [] for pmid in pmids: metadata = get_pubmed_metadata(pmid) if metadata: articles.append(metadata) return articles def search_advanced(term, title, author, journal, start_date, end_date, num_results): # 生成搜索 URL search_url = generate_pubmed_search_url(term=term, title=title, author=author, journal=journal, start_date=start_date, end_date=end_date, num_results=num_results) print("Generated URL:", search_url) # 获取并解析搜索结果 pmids = search_pubmed(search_url) articles = [] for pmid in pmids: metadata = get_pubmed_metadata(pmid) if metadata: articles.append(metadata) return articles if __name__ == "__main__": print("PubMed Search and Analysis Example") # 1. Search for articles print("\n1. Searching for articles about 'COVID-19 vaccine'") articles = search_key_words("COVID-19 vaccine", num_results=5) print("\nSearch Results:") for i, article in enumerate(articles, 1): print(f"{i}. Title: {article['Title']}") print(f" Authors: {article['Authors']}") print(f" PMID: {article['PMID']}") print(f" Journal: {article['Journal']}") print(f" Publication Date: {article['Publication Date']}") print(f" Abstract: {article['Abstract'][:200]}...") # Print first 200 characters of abstract print("---") # 2. Search for articles using advanced search print("\n2. Searching for articles using advanced search") articles = search_advanced(term="COVID-19", title="vaccine", author="Smith", journal="Nature", start_date="2020", end_date="2021", num_results=5) for i, article in enumerate(articles, 1): print(f"{i}. Title: {article['Title']}") print(f" Authors: {article['Authors']}") print(f" PMID: {article['PMID']}") print(f" Journal: {article['Journal']}") print(f" Publication Date: {article['Publication Date']}") print(f" Abstract: {article['Abstract'][:200]}...") # 3. Download full text PDF if articles: print("\n2. Attempting to download the full text PDF of the first article") download_full_text_pdf(articles[0]['PMID']) # 4. Deep Paper Analysis if articles: print("\n3. Generating prompt for deep analysis of the first article") try: analysis_prompt = deep_paper_analysis(articles[0]) print("\nDeep Paper Analysis Prompt:") print(analysis_prompt) # Save analysis prompt to file filename = f"analysis_prompt_PMID_{articles[0]['PMID']}.txt" with open(filename, 'w') as f: f.write(analysis_prompt) print(f"\nAnalysis prompt saved to {filename}") except Exception as e: print(f"An error occurred while generating the analysis prompt: {str(e)}") else: print("No articles available for analysis.") print("\nExample completed. You can modify the search terms and parameters in the script to explore different results.")