search_google_scholar
Search academic papers from Google Scholar.
Args:
query: Search query string (e.g., 'machine learning').
max_results: Maximum number of papers to return (default: 10).
Returns:
List of paper metadata in dictionary format.
search_iacr
Search academic papers from IACR ePrint Archive.
Args:
query: Search query string (e.g., 'cryptography', 'secret sharing').
max_results: Maximum number of papers to return (default: 10).
fetch_details: Whether to fetch detailed information for each paper (default: True).
Returns:
List of paper metadata in dictionary format.
download_arxiv
Download PDF of an arXiv paper.
Args:
paper_id: arXiv paper ID (e.g., '2106.12345').
save_path: Directory to save the PDF (default: './downloads').
Returns:
Path to the downloaded PDF file.
download_pubmed
Attempt to download PDF of a PubMed paper.
Args:
paper_id: PubMed ID (PMID).
save_path: Directory to save the PDF (default: './downloads').
Returns:
str: Message indicating that direct PDF download is not supported.
download_biorxiv
Download PDF of a bioRxiv paper.
Args:
paper_id: bioRxiv DOI.
save_path: Directory to save the PDF (default: './downloads').
Returns:
Path to the downloaded PDF file.
download_medrxiv
Download PDF of a medRxiv paper.
Args:
paper_id: medRxiv DOI.
save_path: Directory to save the PDF (default: './downloads').
Returns:
Path to the downloaded PDF file.
download_iacr
Download PDF of an IACR ePrint paper.
Args:
paper_id: IACR paper ID (e.g., '2009/101').
save_path: Directory to save the PDF (default: './downloads').
Returns:
Path to the downloaded PDF file.
read_arxiv_paper
Read and extract text content from an arXiv paper PDF.
Args:
paper_id: arXiv paper ID (e.g., '2106.12345').
save_path: Directory where the PDF is/will be saved (default: './downloads').
Returns:
str: The extracted text content of the paper.
read_pubmed_paper
Read and extract text content from a PubMed paper.
Args:
paper_id: PubMed ID (PMID).
save_path: Directory where the PDF would be saved (unused).
Returns:
str: Message indicating that direct paper reading is not supported.
read_biorxiv_paper
Read and extract text content from a bioRxiv paper PDF.
Args:
paper_id: bioRxiv DOI.
save_path: Directory where the PDF is/will be saved (default: './downloads').
Returns:
str: The extracted text content of the paper.
read_medrxiv_paper
Read and extract text content from a medRxiv paper PDF.
Args:
paper_id: medRxiv DOI.
save_path: Directory where the PDF is/will be saved (default: './downloads').
Returns:
str: The extracted text content of the paper.
read_iacr_paper
Read and extract text content from an IACR ePrint paper PDF.
Args:
paper_id: IACR paper ID (e.g., '2009/101').
save_path: Directory where the PDF is/will be saved (default: './downloads').
Returns:
str: The extracted text content of the paper.
search_semantic
Search academic papers from Semantic Scholar.
Args:
query: Search query string (e.g., 'machine learning').
year: Optional year filter (e.g., '2019', '2016-2020', '2010-', '-2015').
max_results: Maximum number of papers to return (default: 10).
Returns:
List of paper metadata in dictionary format.
download_semantic
Download PDF of a Semantic Scholar paper.
Args:
paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats:
- Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b")
- DOI:<doi> (e.g., "DOI:10.18653/v1/N18-3011")
- ARXIV:<id> (e.g., "ARXIV:2106.15928")
- MAG:<id> (e.g., "MAG:112218234")
- ACL:<id> (e.g., "ACL:W12-3903")
- PMID:<id> (e.g., "PMID:19872477")
- PMCID:<id> (e.g., "PMCID:2323736")
- URL:<url> (e.g., "URL:https://arxiv.org/abs/2106.15928v1")
save_path: Directory to save the PDF (default: './downloads').
Returns:
Path to the downloaded PDF file.
read_semantic_paper
Read and extract text content from a Semantic Scholar paper.
Args:
paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats:
- Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b")
- DOI:<doi> (e.g., "DOI:10.18653/v1/N18-3011")
- ARXIV:<id> (e.g., "ARXIV:2106.15928")
- MAG:<id> (e.g., "MAG:112218234")
- ACL:<id> (e.g., "ACL:W12-3903")
- PMID:<id> (e.g., "PMID:19872477")
- PMCID:<id> (e.g., "PMCID:2323736")
- URL:<url> (e.g., "URL:https://arxiv.org/abs/2106.15928v1")
save_path: Directory where the PDF is/will be saved (default: './downloads').
Returns:
str: The extracted text content of the paper.
search_crossref
Search academic papers from CrossRef database.
CrossRef is a scholarly infrastructure organization that provides
persistent identifiers (DOIs) for scholarly content and metadata.
It's one of the largest citation databases covering millions of
academic papers, journals, books, and other scholarly content.
Args:
query: Search query string (e.g., 'machine learning', 'climate change').
max_results: Maximum number of papers to return (default: 10, max: 1000).
**kwargs: Additional search parameters:
- filter: CrossRef filter string (e.g., 'has-full-text:true,from-pub-date:2020')
- sort: Sort field ('relevance', 'published', 'updated', 'deposited', etc.)
- order: Sort order ('asc' or 'desc')
Returns:
List of paper metadata in dictionary format.
Examples:
# Basic search
search_crossref("deep learning", 20)
# Search with filters
search_crossref("climate change", 10, filter="from-pub-date:2020,has-full-text:true")
# Search sorted by publication date
search_crossref("neural networks", 15, sort="published", order="desc")
get_crossref_paper_by_doi
Get a specific paper from CrossRef by its DOI.
Args:
doi: Digital Object Identifier (e.g., '10.1038/nature12373').
Returns:
Paper metadata in dictionary format, or empty dict if not found.
Example:
get_crossref_paper_by_doi("10.1038/nature12373")
download_crossref
Attempt to download PDF of a CrossRef paper.
Args:
paper_id: CrossRef DOI (e.g., '10.1038/nature12373').
save_path: Directory to save the PDF (default: './downloads').
Returns:
str: Message indicating that direct PDF download is not supported.
Note:
CrossRef is a citation database and doesn't provide direct PDF downloads.
Use the DOI to access the paper through the publisher's website.
read_crossref_paper
Attempt to read and extract text content from a CrossRef paper.
Args:
paper_id: CrossRef DOI (e.g., '10.1038/nature12373').
save_path: Directory where the PDF is/will be saved (default: './downloads').
Returns:
str: Message indicating that direct paper reading is not supported.
Note:
CrossRef is a citation database and doesn't provide direct paper content.
Use the DOI to access the paper through the publisher's website.