About adamamer20-paper-search-mcp-openai
A Python MCP server for researchers and AI workflows to search and download papers across academic sources. It returns standardized paper records and includes the `search` and `fetch` tools used by OpenAI Deep Research and ChatGPT connectors.
Key features
- 01Search papers across academic sources
- 02Download paper PDFs
- 03Standardized paper records
- 04Asynchronous network requests
- 05Extensible platform modules
Use cases
- 01Find papers across research platforms
- 02Retrieve PDFs for literature review
- 03Provide paper search to AI workflows
MCP support
Exposes paper search and download tools to MCP clients, including the standardized `search` and `fetch` tools for OpenAI Deep Research and ChatGPT connectors.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcpclaude mcp add --transport http ai-smithery-adamamer20-paper-search-mcp-openai https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp{"mcpServers":{"ai-smithery-adamamer20-paper-search-mcp-openai":{"type":"http","url":"https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp"}}}Open in Cursor{"servers":{"ai-smithery-adamamer20-paper-search-mcp-openai":{"type":"http","url":"https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp"}}}Open in VS Code{"mcpServers":{"ai-smithery-adamamer20-paper-search-mcp-openai":{"type":"http","url":"https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp"}}}{"mcpServers":{"ai-smithery-adamamer20-paper-search-mcp-openai":{"serverUrl":"https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcpAuth requiredTools
Details
From the project READMEPaper 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.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.