Academic

Academic MCP server

python

2

GitHub Stars

python

Language

4 months ago

First Indexed

3 weeks ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "nanyang12138-academic-mcp-server": {
      "command": "C:\\Users\\YOUR_USERNAME\\path\\to\\Academic-MCP-Server\\venv\\Scripts\\python.exe",
      "args": [
        "C:\\Users\\YOUR_USERNAME\\path\\to\\Academic-MCP-Server\\academic_server.py"
      ]
    }
  }
}

You operate an Academic MCP Server that lets AI assistants access multiple scholarly databases through a single, consistent interface. It enables unified search, metadata retrieval, PDF access, and advanced research workflows across sources like PubMed, bioRxiv, medRxiv, arXiv, Semantic Scholar, and Sci-Hub, making it easier to retrieve, analyze, and summarize academic papers.

How to use

You interact with the server using an MCP client to perform cross-database searches, retrieve paper details, download PDFs, and run deep analyses. Start with a basic search to discover relevant papers, then refine results with advanced filters such as title, author, date, and journal. Use the advanced analysis features to extract key information, analyze citation networks, and generate comprehensive summaries. You can perform a complete research workflow in a single sequence: retrieve → analyze → read → summarize, using both basic and advanced MCP servers as needed.

How to install

Prerequisites: Python 3.10 or newer and internet access.

Install dependencies and set up the environment with the commands below.

Additional sections

Configuration for the MCP servers is provided to run locally. There are two complementary MCP servers available: a basic server for search and retrieval across six databases, and an advanced server for citation analysis, impact evaluation, local PDF analysis, and complete research workflows.

Security and usage notes: keep your environment secure, manage API keys and credentials as needed, and respect copyright and access policies when downloading papers. For local PDF processing, you can analyze both local files and online PDFs, extract full text, and parse sections and figures.

Troubleshooting tips: ensure dependencies are installed, verify the MCP configuration, and check logs for errors. If a specific database access fails, confirm network access and any required API keys or access policies for that source.

Available tools

search_papers

Search for papers using keywords across all databases or a specific source, returning a list of results with basic metadata.

search_papers_advanced

Perform an advanced search with filters such as title, author, journal, date range, and source to refine results.

get_paper_metadata

Retrieve detailed metadata for a specific paper by its identifier and source.

download_paper_pdf

Download the PDF for a specified paper when available.

list_available_sources

List all databases supported by the MCP server.

deep_paper_analysis

Generate and return a deep analysis prompt for a given paper to support thorough understanding.

analyze_citation_network

Analyze a paper’s citation network to understand relationships and influence.

evaluate_paper_impact

Evaluate the academic impact of a paper using citation and related metrics.

recommend_related_papers

Recommend related papers using multiple strategies such as citations, similarity, and influence.

research_workflow_complete

Execute a complete research workflow from topic retrieval to summary generation.

analyze_local_paper

Comprehensively analyze a local or online PDF paper, including figures and sections.

list_all_figures

List all figures extracted from a PDF paper.

explain_specific_figure

Explain a specific figure from a PDF with optional context.

extract_text_from_pdf

Extract full text from a PDF, with optional section parsing and page range.

batch_analyze_local_papers

Batch analyze multiple PDFs from a local folder.

compare_papers

Compare multiple papers across chosen aspects such as methodology and findings.

extract_key_information

Extract key information like methodology, findings, limitations, and datasets from papers.

generate_paper_summary

Automatically generate summaries in various styles (brief, comprehensive, technical, layman).

extract_pdf_fulltext

Extract full text content from a PDF file URL with optional section extraction.