Academia MCP
Enables searching, fetching, and analyzing scientific papers from ArXiv, ACL Anthology, Semantic Scholar, and Hugging Face datasets, with optional LLM-powered document QA and research proposal workflows.
What is Academia MCP?
Academia MCP is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to enables searching, fetching, and analyzing scientific papers from arxiv, acl anthology, semantic scholar, and hugging face datasets, with optional llm-powered document qa and research proposal workflo...
Enables searching, fetching, and analyzing scientific papers from ArXiv, ACL Anthology, Semantic Scholar, and Hugging Face datasets, with optional LLM-powered document QA and research proposal workflows.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Enables searching, fetching, and analyzing scientific papers
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx academia-mcpConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Academia MCP
Academia MCP connects AI assistants to major scientific research databases including ArXiv, ACL Anthology, Semantic Scholar, and Hugging Face datasets, enabling search, download, and analysis of academic papers without leaving your chat interface. It supports optional LLM-powered workflows for document Q&A, research proposal generation, and automated paper reviews when configured with an OpenRouter API key. Researchers and developers use it to accelerate literature reviews, extract citations, compile bibliographies, and generate new research directions—all from a conversational interface. The server supports stdio, SSE, and streamable-HTTP transports and can run entirely without an LLM key for basic search and retrieval tasks.
Prerequisites
- Python 3.12 or higher installed
- pip or uv package manager available
- OPENROUTER_API_KEY if using LLM-powered tools (document QA, research proposals)
- Optional: TAVILY_API_KEY, EXA_API_KEY, or BRAVE_API_KEY for web search features
- An MCP-compatible client such as Claude Desktop or Claude Code
Install the package
Install academia-mcp from PyPI using pip. Python 3.12 or higher is required.
pip install academia-mcpSet environment variables
Export at minimum the OPENROUTER_API_KEY for LLM-powered features. Additional search API keys are optional but unlock broader web search capabilities.
export OPENROUTER_API_KEY=your_openrouter_key
export TAVILY_API_KEY=your_tavily_key # optional
export WORKSPACE_DIR=/tmp/academia_workspaceTest the server in stdio mode
Run the server locally in stdio mode to confirm it starts correctly before wiring it into your MCP client.
python -m academia_mcp --transport stdioAdd the server to your MCP client configuration
Edit your MCP client's config file (e.g. claude_desktop_config.json) to register academia-mcp under mcpServers.
Restart your MCP client
Save the config file and restart Claude Desktop or your MCP client. The academia server will appear in the available tools list.
Academia MCP Examples
Client configuration
Claude Desktop configuration for academia-mcp using stdio transport with optional environment variables.
{
"mcpServers": {
"academia": {
"command": "python3",
"args": ["-m", "academia_mcp", "--transport", "stdio"],
"env": {
"OPENROUTER_API_KEY": "your_openrouter_key",
"TAVILY_API_KEY": "your_tavily_key",
"WORKSPACE_DIR": "/tmp/academia_workspace"
}
}
}
}Prompts to try
Example prompts for searching papers, extracting citations, and running LLM-powered research workflows.
- "Search ArXiv for recent papers on retrieval-augmented generation published in 2024"
- "Find ACL Anthology papers about instruction tuning for large language models"
- "Download the PDF for ArXiv paper 2305.10601 and answer: what datasets did they use?"
- "Search Semantic Scholar for papers citing 'Attention is All You Need' and summarize the top 5"
- "Generate a research proposal combining diffusion models and code generation"Troubleshooting Academia MCP
LLM-powered tools fail with an authentication error
Ensure OPENROUTER_API_KEY is correctly exported in your shell or set in the MCP client's env config. Document QA and research proposal tools require this key.
Server starts but web search tools return no results
Web search requires at least one of TAVILY_API_KEY, EXA_API_KEY, or BRAVE_API_KEY to be set. Set the relevant key in your environment and restart the server.
Python version error on startup
Academia MCP requires Python 3.12+. Run `python3 --version` to check. Install the latest Python from python.org or use pyenv to manage versions.
Frequently Asked Questions about Academia MCP
What is Academia MCP?
Academia MCP is a Model Context Protocol (MCP) server that enables searching, fetching, and analyzing scientific papers from arxiv, acl anthology, semantic scholar, and hugging face datasets, with optional llm-powered document qa and research proposal workflows. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Academia MCP?
Follow the installation instructions on the Academia MCP GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Academia MCP?
Academia MCP works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Academia MCP free to use?
Yes, Academia MCP is open source and available under the Apache 2.0 license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
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