Google-Scholar-MCP-Server

import requests from bs4 import BeautifulSoup import time from scholarly import scholarly # 普通关键词搜索函数 def google_scholar_search(query, num_results=5): """ Function to search Google Scholar using a simple keyword query. Parameters: query (str): The search query (e.g., paper title or author). num_results (int): The number of results to retrieve. Returns: list: A list of dictionaries containing search results. """ # Prepare the search URL search_url = f"https://scholar.google.com/scholar?q={query.replace(' ', '+')}" # Set up headers to mimic a real browser request 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" } # Send the GET request to Google Scholar response = requests.get(search_url, headers=headers) # Check if the request was successful if response.status_code != 200: print(f"Failed to fetch data. HTTP Status code: {response.status_code}") return [] # Parse the HTML content using BeautifulSoup soup = BeautifulSoup(response.text, 'html.parser') # Find all the articles in the search results results = [] count = 0 # Find the results on the page for item in soup.find_all('div', class_='gs_ri'): if count >= num_results: break title_tag = item.find('h3', class_='gs_rt') title = title_tag.get_text() if title_tag else 'No title available' link = title_tag.find('a')['href'] if title_tag and title_tag.find('a') else 'No link available' authors_tag = item.find('div', class_='gs_a') authors = authors_tag.get_text() if authors_tag else 'No authors available' abstract_tag = item.find('div', class_='gs_rs') abstract = abstract_tag.get_text() if abstract_tag else 'No abstract available' result_data = { 'Title': title, 'Authors': authors, 'Abstract': abstract, 'URL': link } results.append(result_data) count += 1 return results # 高级搜索函数 def advanced_google_scholar_search(query, author=None, year_range=None, num_results=5): """ Function to search Google Scholar using advanced search filters (e.g., author, year range). Parameters: query (str): The search query (e.g., paper title or topic). author (str): The author's name to filter the results (default is None). year_range (tuple): A tuple (start_year, end_year) to filter the results by publication year (default is None). num_results (int): The number of results to retrieve. Returns: list: A list of dictionaries containing search results. """ # Prepare the advanced search URL search_url = "https://scholar.google.com/scholar?" # Build the search query search_params = {'q': query.replace(' ', '+')} if author: search_params['as_auth'] = author if year_range: start_year, end_year = year_range search_params['as_ylo'] = start_year # Start year search_params['as_yhi'] = end_year # End year # Encode the search parameters into the URL search_url += '&'.join([f"{key}={value}" for key, value in search_params.items()]) # Set up headers to mimic a real browser request 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" } # Send the GET request to Google Scholar response = requests.get(search_url, headers=headers) # Check if the request was successful if response.status_code != 200: print(f"Failed to fetch data. HTTP Status code: {response.status_code}") return [] # Parse the HTML content using BeautifulSoup soup = BeautifulSoup(response.text, 'html.parser') # Find all the articles in the search results results = [] count = 0 # Find the results on the page for item in soup.find_all('div', class_='gs_ri'): if count >= num_results: break title_tag = item.find('h3', class_='gs_rt') title = title_tag.get_text() if title_tag else 'No title available' link = title_tag.find('a')['href'] if title_tag and title_tag.find('a') else 'No link available' authors_tag = item.find('div', class_='gs_a') authors = authors_tag.get_text() if authors_tag else 'No authors available' abstract_tag = item.find('div', class_='gs_rs') abstract = abstract_tag.get_text() if abstract_tag else 'No abstract available' result_data = { 'Title': title, 'Authors': authors, 'Abstract': abstract, 'URL': link } results.append(result_data) count += 1 return results # Example usage: if __name__ == "__main__": # 1.普通关键词搜索 query = "machine learning" results = google_scholar_search(query, num_results=5) print("Results for keyword search:") for result in results: print(f"\nTitle: {result['Title']}") print(f"Authors: {result['Authors']}") print(f"Abstract: {result['Abstract']}") print(f"URL: {result['URL']}") print("-" * 80) # 2.高级搜索 advanced_query = "machine learning" advanced_results = advanced_google_scholar_search(advanced_query, author="Ian Goodfellow", year_range=(2010, 2021), num_results=5) print("\nResults for advanced search:") for result in advanced_results: print(f"\nTitle: {result['Title']}") print(f"Authors: {result['Authors']}") print(f"Abstract: {result['Abstract']}") print(f"URL: {result['URL']}") print("-" * 80) # Retrieve the author's data, fill-in, and print # 3.Get an iterator for the author results search_query = scholarly.search_author('Steven A Cholewiak') # 4.Retrieve the first result from the iterator first_author_result = next(search_query) scholarly.pprint(first_author_result) # 5.Retrieve all the details for the author author = scholarly.fill(first_author_result ) scholarly.pprint(author) # 6.Take a closer look at the first publication first_publication = author['publications'][0] first_publication_filled = scholarly.fill(first_publication) scholarly.pprint(first_publication_filled) # 7.Print the titles of the author's publications publication_titles = [pub['bib']['title'] for pub in author['publications']] print(publication_titles)