Adamamer20 Paper Search Mcp Openai
1325 toolsauthHTTPregistry active
Summary
Connects to arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, and IACR ePrint Archive for academic paper searches and PDF downloads. Exposes platform-specific tools like search_arxiv and download_arxiv, plus standardized search and fetch tools that work with OpenAI Deep Research and ChatGPT connectors. Returns papers in a consistent dictionary format via a Paper class. You'd reach for this when building research workflows that need to pull papers from multiple sources without writing custom scrapers for each one. Optional Semantic Scholar API key unlocks enhanced features. Built in Python with async httpx calls and designed to extend with new platforms.
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Tools
Public tool metadata for what this MCP can expose to an agent.
25 tools
searchDeep Research compatible search tool aggregating across sources.2 params
Deep Research compatible search tool aggregating across sources.
Parameters* required
querystringmax_resultsintegerdefault: 15
fetchFetch full document content for a search result.2 params
Fetch full document content for a search result.
Parameters* required
idvaluedocument_idvaluesearch_arxivSearch academic papers from arXiv. 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.2 params
Search academic papers from arXiv. 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.
Parameters* required
querystringmax_resultsintegerdefault: 10
search_pubmedSearch academic papers from PubMed. 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.2 params
Search academic papers from PubMed. 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.
Parameters* required
querystringmax_resultsintegerdefault: 10
search_biorxivSearch academic papers from bioRxiv. 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.2 params
Search academic papers from bioRxiv. 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.
Parameters* required
querystringmax_resultsintegerdefault: 10
search_medrxivSearch academic papers from medRxiv. 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.2 params
Search academic papers from medRxiv. 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.
Parameters* required
querystringmax_resultsintegerdefault: 10
search_google_scholarSearch 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.2 params
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.
Parameters* required
querystringmax_resultsintegerdefault: 10
search_iacrSearch 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). Retur...3 params
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). Retur...
Parameters* required
querystringmax_resultsintegerdefault: 10
fetch_detailsbooleandefault: true
download_arxivDownload 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
download_pubmedAttempt 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
download_biorxivDownload 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
download_medrxivDownload 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
download_iacrDownload 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_arxiv_paperRead 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_pubmed_paperRead 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_biorxiv_paperRead 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_medrxiv_paperRead 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_iacr_paperRead 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.2 params
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.
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
search_semanticSearch 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 i...3 params
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 i...
Parameters* required
yearvaluequerystringmax_resultsintegerdefault: 10
download_semanticDownload 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., "A...2 params
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., "A...
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_semantic_paperRead 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") - A...2 params
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") - A...
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
search_crossrefSearch 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,...3 params
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,...
Parameters* required
querystringkwargsstringmax_resultsintegerdefault: 10
get_crossref_paper_by_doiGet 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")1 params
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")
Parameters* required
doistringdownload_crossrefAttempt 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 data...2 params
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 data...
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
read_crossref_paperAttempt 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...2 params
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...
Parameters* required
paper_idstringsave_pathstringdefault: ./downloads
Paper Search MCP
A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources, including arXiv, PubMed, bioRxiv, and Sci-Hub (optional). Designed for seamless integration with large language models like Claude Desktop.
Table of Contents
Overview
paper-search-mcp is a Python-based MCP server that enables users to search and download academic papers from various platforms. It provides tools for searching papers (e.g., search_arxiv) and downloading PDFs (e.g., download_arxiv), making it ideal for researchers and AI-driven workflows. Built with the MCP Python SDK, it integrates seamlessly with LLM clients like Claude Desktop.
Features
- Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar.
- Deep Research Ready: Provides the standardized
searchandfetchtools required by OpenAI Deep Research and ChatGPT connectors. - Standardized Output: Papers are returned in a consistent dictionary format via the
Paperclass. - Asynchronous Tools: Efficiently handles network requests using
httpx. - MCP Integration: Compatible with MCP clients for LLM context enhancement.
- Extensible Design: Easily add new academic platforms by extending the
academic_platformsmodule.
Installation
paper-search-mcp can be installed using uv or pip. Below are two approaches: a quick start for immediate use and a detailed setup for development.
Installing via Smithery
To install paper-search-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @openags/paper-search-mcp --client claude
Quick Start
For users who want to quickly run the server:
-
Install Package:
uv add paper-search-mcp -
Configure Claude Desktop: Add this configuration to
~/Library/Application Support/Claude/claude_desktop_config.json(Mac) or%APPDATA%\Claude\claude_desktop_config.json(Windows):{ "mcpServers": { "paper_search_server": { "command": "uv", "args": [ "run", "--directory", "/path/to/your/paper-search-mcp", "-m", "paper_search_mcp.server" ], "env": { "SEMANTIC_SCHOLAR_API_KEY": "" // Optional: For enhanced Semantic Scholar features } } } }Note: Replace
/path/to/your/paper-search-mcpwith your actual installation path.
For Development
For developers who want to modify the code or contribute:
-
Setup Environment:
# Install uv if not installed curl -LsSf https://astral.sh/uv/install.sh | sh # Clone repository git clone https://github.com/openags/paper-search-mcp.git cd paper-search-mcp # Create and activate virtual environment uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate -
Install Dependencies:
# Install project in editable mode uv add -e . # Add development dependencies (optional) uv add pytest flake8
Contributing
We welcome contributions! Here's how to get started:
-
Fork the Repository: Click "Fork" on GitHub.
-
Clone and Set Up:
git clone https://github.com/yourusername/paper-search-mcp.git cd paper-search-mcp pip install -e ".[dev]" # Install dev dependencies (if added to pyproject.toml) -
Make Changes:
- Add new platforms in
academic_platforms/. - Update tests in
tests/.
- Add new platforms in
-
Submit a Pull Request: Push changes and create a PR on GitHub.
Demo
TODO
Planned Academic Platforms
- [√] arXiv
- [√] PubMed
- [√] bioRxiv
- [√] medRxiv
- [√] Google Scholar
- [√] IACR ePrint Archive
- [√] Semantic Scholar
- PubMed Central (PMC)
- Science Direct
- Springer Link
- IEEE Xplore
- ACM Digital Library
- Web of Science
- Scopus
- JSTOR
- ResearchGate
- CORE
- Microsoft Academic
License
This project is licensed under the MIT License. See the LICENSE file for details.
Happy researching with paper-search-mcp! If you encounter issues, open a GitHub issue.
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UpdatedSep 18, 2025
