Academic MCP Server

🔍 A unified Model Context Protocol (MCP) server that provides AI assistants access to multiple academic databases through a single, consistent interface.

🌟 Features

Supported Databases

PubMed 🏥 - Biomedical and life sciences literature (NCBI)
bioRxiv 🧬 - Biology preprints
medRxiv 💊 - Medical preprints
arXiv 🔬 - Physics, mathematics, computer science, and more
Semantic Scholar 🤖 - AI-powered academic search across disciplines
Sci-Hub 📚 - Comprehensive academic paper access and download

Core Capabilities

✅ Unified Search: Search across all databases with a single query
✅ Advanced Filtering: Filter by title, author, date, journal, and more
✅ Metadata Access: Retrieve detailed paper information
✅ PDF Download: Download open access papers when available
✅ Deep Analysis: Generate comprehensive paper analysis prompts
✅ Local PDF Analysis: Support for both local and online PDF file analysis
✅ Citation Network Analysis: Analyze paper citation relationships and impact
✅ Complete Research Workflow: One-click retrieve→analyze→read→summarize
✅ Standardized Output: Consistent data format across all sources

🚀 Quick Start

Prerequisites

Python 3.10+
MCP library
Internet connection

Installation

✅ Already Installed! Your Academic MCP Server is fully configured and ready to use.

If you need to set it up on another machine:

Clone or download this repository:
```
cd Academic-MCP-Server
```
Create a virtual environment:
```
python -m venv venv
```
Activate the virtual environment:
- Windows: venv\Scripts\activate
- Mac/Linux: source venv/bin/activate
Install dependencies:
```
pip install -r requirements.txt
```

Note: All PubMed functionality is integrated locally. No external dependencies required!

Configuration for Cursor

This project provides TWO MCP servers with complementary features:

academic - Basic search, metadata retrieval, and PDF downloads across 6 databases (PubMed, bioRxiv, medRxiv, arXiv, Semantic Scholar, Sci-Hub)
academic-research - Advanced features including citation analysis, paper impact evaluation, local PDF analysis, and complete research workflows

Add this configuration to your MCP settings file (~/.cursor/mcp.json or C:\Users\YOUR_USERNAME\.cursor\mcp.json):

Windows:

{
  "mcpServers": {
    "academic": {
      "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"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    },
    "academic-research": {
      "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_research_advanced.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

Mac/Linux:

{
  "mcpServers": {
    "academic": {
      "command": "/path/to/Academic-MCP-Server/venv/bin/python",
      "args": [
        "/path/to/Academic-MCP-Server/academic_server.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    },
    "academic-research": {
      "command": "/path/to/Academic-MCP-Server/venv/bin/python",
      "args": [
        "/path/to/Academic-MCP-Server/academic_research_advanced.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

Note: Replace YOUR_USERNAME and path/to with your actual paths.

📖 Usage

Search Papers

Search across all databases:

search_papers(
    keywords="UCAR-T",
    source="all",
    num_results=15
)

Search specific database:

search_papers(
    keywords="machine learning",
    source="arxiv",
    num_results=10
)

Advanced Search

search_papers_advanced(
    title="neural networks",
    author="Hinton",
    start_date="2020-01-01",
    end_date="2024-12-31",
    source="semantic_scholar",
    num_results=10
)

PubMed-specific advanced search:

search_papers_advanced(
    title="CAR-T",
    author="Wang",
    journal="Nature",
    start_date="2024/01/01",  # PubMed uses YYYY/MM/DD
    end_date="2025/12/31",
    source="pubmed",
    num_results=10
)

Get Paper Metadata

# PubMed
get_paper_metadata(identifier="40883768", source="pubmed")

# bioRxiv
get_paper_metadata(identifier="10.1101/2024.01.001", source="biorxiv")

# arXiv
get_paper_metadata(identifier="2301.00001", source="arxiv")

# Semantic Scholar (Paper ID or DOI)
get_paper_metadata(identifier="DOI:10.1038/s41586-020-1234-5", source="semantic_scholar")

Download PDF

download_paper_pdf(identifier="2301.00001", source="arxiv")

List Available Sources

list_available_sources()
# Returns: ["pubmed", "biorxiv", "medrxiv", "arxiv", "semantic_scholar", "scihub"]

Deep Paper Analysis

deep_paper_analysis(identifier="40883768", source="pubmed")

🛠 MCP Tools Reference

Server: `academic` (Basic Search & Retrieval)

1. `search_papers`

Search for papers using keywords.

Parameters:

keywords (str): Search query
source (str): "all", "pubmed", "biorxiv", "medrxiv", "arxiv", "semantic_scholar", or "scihub"
num_results (int): Number of results per source (default: 10)

2. `search_papers_advanced`

Advanced search with multiple filters.

Parameters:

title (str, optional): Search in titles
author (str, optional): Author name
journal (str, optional): Journal name
start_date (str, optional): Start date
end_date (str, optional): End date
term (str, optional): General search term
source (str): Database source
num_results (int): Number of results

3. `get_paper_metadata`

Get detailed metadata for a specific paper.

Parameters:

identifier (str): Paper ID (PMID, DOI, arXiv ID, etc.)
source (str): Database source

4. `download_paper_pdf`

Download PDF for a paper.

Parameters:

identifier (str): Paper ID
source (str): Database source

5. `list_available_sources`

List all available databases.

6. `deep_paper_analysis`

Generate comprehensive analysis prompt.

Parameters:

identifier (str): Paper ID
source (str): Database source

Server: `academic-research` (Advanced Analysis & Research)

1. `analyze_citation_network`

Analyze paper's citation network.

Parameters:

paper_id (str): Paper identifier (DOI, PMID, etc.)
source (str): Data source (default: "semantic_scholar")
max_depth (int): Network depth 1-3 layers (default: 2)

2. `evaluate_paper_impact`

Evaluate academic impact of a paper.

Parameters:

paper_id (str): Paper identifier
source (str): Data source (default: "semantic_scholar")

3. `recommend_related_papers`

Recommend related papers using multiple strategies.

Parameters:

paper_id (str): Source paper identifier
source (str): Data source (default: "semantic_scholar")
num_recommendations (int): Number of recommendations (default: 10)
strategy (str): "comprehensive", "citations", "similar", or "influential"

4. `research_workflow_complete`

⭐ Recommended Core Feature - Complete research workflow: retrieve → analyze → read → summarize

Parameters:

topic (str): Research topic (e.g., "CRISPR gene editing")
num_papers (int): Number of papers to retrieve (default: 5)
include_analysis (bool): Include deep analysis (default: true)
include_summary (bool): Include auto-summary (default: true)

5. `analyze_local_paper`

Comprehensively analyze local or online PDF papers.

Parameters:

pdf_path (str): PDF file path (local or URL)
include_figures (bool): Analyze figures (default: true)
include_summary (bool): Generate summary (default: true)

6. `list_all_figures`

List all figures from a PDF paper.

Parameters:

pdf_path (str): PDF file path (local or URL)

7. `explain_specific_figure`

Explain a specific figure from a PDF.

Parameters:

pdf_path (str): PDF file path (local or URL)
figure_number (int): Figure number (e.g., 1, 2, 3)
provide_context (bool): Include context paragraphs (default: true)

8. `extract_text_from_pdf`

Extract text content from PDF (supports both local and online URLs).

Parameters:

pdf_path (str): PDF path (local or URL)
extract_sections (bool): Whether to extract by sections
page_range (tuple, optional): Page range, e.g., (1, 10) for pages 1-10

9. `batch_analyze_local_papers`

Batch analyze all PDF papers in a folder (local folders only).

Parameters:

folder_path (str): Folder path
max_papers (int): Maximum number of papers to analyze (default: 10)
file_pattern (str): File matching pattern (default: "*.pdf")

10. `compare_papers`

Compare multiple papers.

Parameters:

paper_ids (list): List of paper IDs to compare (2-5 papers)
comparison_aspects (list, optional): Comparison dimensions - "methodology", "findings", "impact", "timeline"

11. `extract_key_information`

Extract key information from papers.

Parameters:

paper_id (str): Paper identifier
source (str): Data source (default: "semantic_scholar")
info_types (list, optional): List of information types to extract
- "methodology": Research methods
- "findings": Main findings
- "limitations": Study limitations
- "datasets": Used datasets
- "metrics": Evaluation metrics
- "contributions": Main contributions

12. `generate_paper_summary`

Automatically generate paper summaries.

Parameters:

paper_id (str): Paper identifier
source (str): Data source (default: "semantic_scholar")
summary_type (str): Summary type
- "brief": Brief summary (100-200 words)
- "comprehensive": Comprehensive summary (500-800 words)
- "technical": Technical details summary
- "layman": Easy-to-understand version

13. `extract_pdf_fulltext`

Extract full text content from PDF.

Parameters:

pdf_url (str): PDF file URL
extract_sections (bool): Whether to identify and extract sections (default: true)

📊 Standardized Output Format

All search results return papers in this standardized format:

{
    "id": "Unique identifier (PMID, DOI, arXiv ID, etc.)",
    "title": "Paper title",
    "authors": "Author names (comma-separated)",
    "abstract": "Paper abstract",
    "publication_date": "Publication date",
    "journal": "Journal or venue name",
    "url": "Link to paper",
    "pdf_url": "PDF link (if available)",
    "source": "Database source (pubmed/biorxiv/arxiv/etc.)"
}

Semantic Scholar results include additional fields:

citation_count: Number of citations
reference_count: Number of references
fields_of_study: Research areas

🔧 Architecture

Dual Server Design

This project provides two complementary MCP servers:

academic_server.py - Core search and retrieval functionality
academic_research_advanced.py - Advanced analysis and research workflows

Project Structure

Academic-MCP-Server/
├── academic_server.py          # Main MCP server (basic search)
├── academic_research_advanced.py # Advanced research server
├── adapters/                   # Database adapters
│   ├── base_adapter.py        # Abstract base class
│   ├── pubmed_adapter.py      # PubMed wrapper
│   ├── biorxiv_adapter.py     # bioRxiv/medRxiv
│   ├── arxiv_adapter.py       # arXiv
│   ├── semantic_scholar_adapter.py
│   └── scihub_adapter.py      # Sci-Hub
├── utils/                      # Helper functions
│   ├── helpers.py             # General utilities
│   └── pubmed_utils.py        # PubMed-specific utilities
├── requirements.txt           # Dependencies
└── README.md / README_CN.md   # Documentation

Adapter Pattern

Each database is wrapped in an adapter that implements a common interface:

Adding New Databases

To add a new database:

Create a new adapter in adapters/
Inherit from BaseAdapter
Implement all required methods
Register in academic_server.py

Example:

# adapters/new_database_adapter.py
from .base_adapter import BaseAdapter

class NewDatabaseAdapter(BaseAdapter):
    def search_by_keywords(self, keywords, num_results):
        # Implementation
        pass
    # ... implement other methods

# In academic_server.py
from adapters.new_database_adapter import NewDatabaseAdapter

adapters = {
    # ... existing adapters
    "new_database": NewDatabaseAdapter()
}

🎯 Use Cases

For Researchers

Search across multiple preprint servers simultaneously
Find papers by specific authors or topics
Download open access papers automatically
Generate literature review materials
Analyze local PDF collections
Perform comprehensive citation network analysis
Generate automated paper summaries

For AI Assistants

Access comprehensive academic knowledge
Provide up-to-date research information
Help with citation and reference management
Analyze research trends and findings
Process and explain figures from academic papers
Conduct complete research workflows automatically

⚠️ Limitations & Notes

API Rate Limits

PubMed: No API key required, but rate-limited
bioRxiv/medRxiv: No authentication required
arXiv: Rate-limited (1 request per 3 seconds recommended)
Semantic Scholar: Free tier has rate limits; get API key for higher limits at https://www.semanticscholar.org/product/api
Sci-Hub: No authentication required; use responsibly

PDF Availability

PubMed: Only PMC open access articles
bioRxiv/medRxiv: All articles are open access
arXiv: All articles are open access
Semantic Scholar: Depends on publisher policies
Sci-Hub: Wide coverage of academic papers (use for research purposes only)

Local PDF Support

Full text extraction: Extract complete text from local or online PDFs
Figure analysis: List and explain figures from PDF papers
Section parsing: Automatically identify and extract paper sections
Batch processing: Analyze multiple PDFs in a folder simultaneously

Date Formats

PubMed: YYYY/MM/DD
Others: YYYY-MM-DD

🤝 Contributing

Contributions are welcome! Feel free to:

Add new database adapters
Improve existing functionality
Fix bugs
Enhance documentation

📄 License

This project builds upon the PubMed-MCP-Server and follows similar open-source principles.

🙏 Acknowledgments

PubMed-MCP-Server for the original PubMed integration
NCBI E-utilities
bioRxiv/medRxiv API
arXiv API
Semantic Scholar API
Sci-Hub MCP Server (JackKuo666/Sci-Hub-MCP-Server)
FastMCP framework

⚠️ Disclaimer

The Sci-Hub integration is provided for research and educational purposes only. Users are responsible for complying with copyright laws and institutional policies in their jurisdiction. The authors do not endorse or encourage copyright infringement. Please support publishers and authors by obtaining papers through legitimate channels when possible.

📊 Project Statistics

Supported Databases: 6 (PubMed, bioRxiv, medRxiv, arXiv, Semantic Scholar, Sci-Hub)
MCP Servers: 2 (academic, academic-research)
Basic MCP Tools: 6
Advanced Research Tools: 15+
Lines of Code: ~3,000
Supported Formats: PDF, metadata, citations, full-text analysis
PDF Support: Both local files and online URLs

🚀 Enhanced Features

Advanced Research Capabilities

Citation Network Analysis: Understand paper relationships and impact
Automated Summarization: Generate summaries in multiple styles
Key Information Extraction: Extract methodology, findings, limitations
Complete Research Workflows: One-click research from topic to summary

PDF Processing

Local and Online Support: Process PDFs from local storage or URLs
Figure Explanation: AI-powered figure analysis and explanation
Section Recognition: Automatic identification of paper sections
Batch Analysis: Process multiple papers simultaneously

Smart Search Features

Concurrent Database Search: Search all databases simultaneously
Intelligent Result Merging: Deduplicate and rank results
Advanced Filtering: Multi-parameter search with date ranges
Source-Specific Optimization: Tailored search for each database

📞 Support

For issues or questions:

Check the documentation above
Review error messages in logs
Ensure all dependencies are installed
Verify your MCP configuration

Happy researching! 📚🔬

Academic MCP Server

🔍 A unified Model Context Protocol (MCP) server that provides AI assistants access to multiple academic databases through a single, consistent interface.

🌟 Features

Supported Databases

PubMed 🏥 - Biomedical and life sciences literature (NCBI)
bioRxiv 🧬 - Biology preprints
medRxiv 💊 - Medical preprints
arXiv 🔬 - Physics, mathematics, computer science, and more
Semantic Scholar 🤖 - AI-powered academic search across disciplines
Sci-Hub 📚 - Comprehensive academic paper access and download

Core Capabilities

✅ Unified Search: Search across all databases with a single query
✅ Advanced Filtering: Filter by title, author, date, journal, and more
✅ Metadata Access: Retrieve detailed paper information
✅ PDF Download: Download open access papers when available
✅ Deep Analysis: Generate comprehensive paper analysis prompts
✅ Local PDF Analysis: Support for both local and online PDF file analysis
✅ Citation Network Analysis: Analyze paper citation relationships and impact
✅ Complete Research Workflow: One-click retrieve→analyze→read→summarize
✅ Standardized Output: Consistent data format across all sources

🚀 Quick Start

Prerequisites

Python 3.10+
MCP library
Internet connection

Installation

✅ Already Installed! Your Academic MCP Server is fully configured and ready to use.

If you need to set it up on another machine:

Clone or download this repository:
```
cd Academic-MCP-Server
```
Create a virtual environment:
```
python -m venv venv
```
Activate the virtual environment:
- Windows: venv\Scripts\activate
- Mac/Linux: source venv/bin/activate
Install dependencies:
```
pip install -r requirements.txt
```

Note: All PubMed functionality is integrated locally. No external dependencies required!

Configuration for Cursor

This project provides TWO MCP servers with complementary features:

academic - Basic search, metadata retrieval, and PDF downloads across 6 databases (PubMed, bioRxiv, medRxiv, arXiv, Semantic Scholar, Sci-Hub)
academic-research - Advanced features including citation analysis, paper impact evaluation, local PDF analysis, and complete research workflows

Add this configuration to your MCP settings file (~/.cursor/mcp.json or C:\Users\YOUR_USERNAME\.cursor\mcp.json):

Windows:

{
  "mcpServers": {
    "academic": {
      "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"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    },
    "academic-research": {
      "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_research_advanced.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

Mac/Linux:

{
  "mcpServers": {
    "academic": {
      "command": "/path/to/Academic-MCP-Server/venv/bin/python",
      "args": [
        "/path/to/Academic-MCP-Server/academic_server.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    },
    "academic-research": {
      "command": "/path/to/Academic-MCP-Server/venv/bin/python",
      "args": [
        "/path/to/Academic-MCP-Server/academic_research_advanced.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

Note: Replace YOUR_USERNAME and path/to with your actual paths.

📖 Usage

Search Papers

Search across all databases:

search_papers(
    keywords="UCAR-T",
    source="all",
    num_results=15
)

Search specific database:

search_papers(
    keywords="machine learning",
    source="arxiv",
    num_results=10
)

Advanced Search

search_papers_advanced(
    title="neural networks",
    author="Hinton",
    start_date="2020-01-01",
    end_date="2024-12-31",
    source="semantic_scholar",
    num_results=10
)

PubMed-specific advanced search:

search_papers_advanced(
    title="CAR-T",
    author="Wang",
    journal="Nature",
    start_date="2024/01/01",  # PubMed uses YYYY/MM/DD
    end_date="2025/12/31",
    source="pubmed",
    num_results=10
)

Get Paper Metadata

# PubMed
get_paper_metadata(identifier="40883768", source="pubmed")

# bioRxiv
get_paper_metadata(identifier="10.1101/2024.01.001", source="biorxiv")

# arXiv
get_paper_metadata(identifier="2301.00001", source="arxiv")

# Semantic Scholar (Paper ID or DOI)
get_paper_metadata(identifier="DOI:10.1038/s41586-020-1234-5", source="semantic_scholar")

Download PDF

download_paper_pdf(identifier="2301.00001", source="arxiv")

List Available Sources

list_available_sources()
# Returns: ["pubmed", "biorxiv", "medrxiv", "arxiv", "semantic_scholar", "scihub"]

Deep Paper Analysis

deep_paper_analysis(identifier="40883768", source="pubmed")

🛠 MCP Tools Reference

Server: `academic` (Basic Search & Retrieval)

1. `search_papers`

Search for papers using keywords.

Parameters:

keywords (str): Search query
source (str): "all", "pubmed", "biorxiv", "medrxiv", "arxiv", "semantic_scholar", or "scihub"
num_results (int): Number of results per source (default: 10)

2. `search_papers_advanced`

Advanced search with multiple filters.

Parameters:

title (str, optional): Search in titles
author (str, optional): Author name
journal (str, optional): Journal name
start_date (str, optional): Start date
end_date (str, optional): End date
term (str, optional): General search term
source (str): Database source
num_results (int): Number of results

3. `get_paper_metadata`

Get detailed metadata for a specific paper.

Parameters:

identifier (str): Paper ID (PMID, DOI, arXiv ID, etc.)
source (str): Database source

4. `download_paper_pdf`

Download PDF for a paper.

Parameters:

identifier (str): Paper ID
source (str): Database source

5. `list_available_sources`

List all available databases.

6. `deep_paper_analysis`

Generate comprehensive analysis prompt.

Parameters:

identifier (str): Paper ID
source (str): Database source

Server: `academic-research` (Advanced Analysis & Research)

1. `analyze_citation_network`

Analyze paper's citation network.

Parameters:

paper_id (str): Paper identifier (DOI, PMID, etc.)
source (str): Data source (default: "semantic_scholar")
max_depth (int): Network depth 1-3 layers (default: 2)

2. `evaluate_paper_impact`

Evaluate academic impact of a paper.

Parameters:

paper_id (str): Paper identifier
source (str): Data source (default: "semantic_scholar")

3. `recommend_related_papers`

Recommend related papers using multiple strategies.

Parameters:

paper_id (str): Source paper identifier
source (str): Data source (default: "semantic_scholar")
num_recommendations (int): Number of recommendations (default: 10)
strategy (str): "comprehensive", "citations", "similar", or "influential"

4. `research_workflow_complete`

⭐ Recommended Core Feature - Complete research workflow: retrieve → analyze → read → summarize

Parameters:

topic (str): Research topic (e.g., "CRISPR gene editing")
num_papers (int): Number of papers to retrieve (default: 5)
include_analysis (bool): Include deep analysis (default: true)
include_summary (bool): Include auto-summary (default: true)

5. `analyze_local_paper`

Comprehensively analyze local or online PDF papers.

Parameters:

pdf_path (str): PDF file path (local or URL)
include_figures (bool): Analyze figures (default: true)
include_summary (bool): Generate summary (default: true)

6. `list_all_figures`

List all figures from a PDF paper.

Parameters:

pdf_path (str): PDF file path (local or URL)

7. `explain_specific_figure`

Explain a specific figure from a PDF.

Parameters:

pdf_path (str): PDF file path (local or URL)
figure_number (int): Figure number (e.g., 1, 2, 3)
provide_context (bool): Include context paragraphs (default: true)

8. `extract_text_from_pdf`

Extract text content from PDF (supports both local and online URLs).

Parameters:

pdf_path (str): PDF path (local or URL)
extract_sections (bool): Whether to extract by sections
page_range (tuple, optional): Page range, e.g., (1, 10) for pages 1-10

9. `batch_analyze_local_papers`

Batch analyze all PDF papers in a folder (local folders only).

Parameters:

folder_path (str): Folder path
max_papers (int): Maximum number of papers to analyze (default: 10)
file_pattern (str): File matching pattern (default: "*.pdf")

10. `compare_papers`

Compare multiple papers.

Parameters:

paper_ids (list): List of paper IDs to compare (2-5 papers)
comparison_aspects (list, optional): Comparison dimensions - "methodology", "findings", "impact", "timeline"

11. `extract_key_information`

Extract key information from papers.

Parameters:

paper_id (str): Paper identifier
source (str): Data source (default: "semantic_scholar")
info_types (list, optional): List of information types to extract
- "methodology": Research methods
- "findings": Main findings
- "limitations": Study limitations
- "datasets": Used datasets
- "metrics": Evaluation metrics
- "contributions": Main contributions

12. `generate_paper_summary`

Automatically generate paper summaries.

Parameters:

paper_id (str): Paper identifier
source (str): Data source (default: "semantic_scholar")
summary_type (str): Summary type
- "brief": Brief summary (100-200 words)
- "comprehensive": Comprehensive summary (500-800 words)
- "technical": Technical details summary
- "layman": Easy-to-understand version

13. `extract_pdf_fulltext`

Extract full text content from PDF.

Parameters:

pdf_url (str): PDF file URL
extract_sections (bool): Whether to identify and extract sections (default: true)

📊 Standardized Output Format

All search results return papers in this standardized format:

{
    "id": "Unique identifier (PMID, DOI, arXiv ID, etc.)",
    "title": "Paper title",
    "authors": "Author names (comma-separated)",
    "abstract": "Paper abstract",
    "publication_date": "Publication date",
    "journal": "Journal or venue name",
    "url": "Link to paper",
    "pdf_url": "PDF link (if available)",
    "source": "Database source (pubmed/biorxiv/arxiv/etc.)"
}

Semantic Scholar results include additional fields:

citation_count: Number of citations
reference_count: Number of references
fields_of_study: Research areas

🔧 Architecture

Dual Server Design

This project provides two complementary MCP servers:

academic_server.py - Core search and retrieval functionality
academic_research_advanced.py - Advanced analysis and research workflows

Project Structure

Academic-MCP-Server/
├── academic_server.py          # Main MCP server (basic search)
├── academic_research_advanced.py # Advanced research server
├── adapters/                   # Database adapters
│   ├── base_adapter.py        # Abstract base class
│   ├── pubmed_adapter.py      # PubMed wrapper
│   ├── biorxiv_adapter.py     # bioRxiv/medRxiv
│   ├── arxiv_adapter.py       # arXiv
│   ├── semantic_scholar_adapter.py
│   └── scihub_adapter.py      # Sci-Hub
├── utils/                      # Helper functions
│   ├── helpers.py             # General utilities
│   └── pubmed_utils.py        # PubMed-specific utilities
├── requirements.txt           # Dependencies
└── README.md / README_CN.md   # Documentation

Adapter Pattern

Each database is wrapped in an adapter that implements a common interface:

Adding New Databases

To add a new database:

Create a new adapter in adapters/
Inherit from BaseAdapter
Implement all required methods
Register in academic_server.py

Example:

# adapters/new_database_adapter.py
from .base_adapter import BaseAdapter

class NewDatabaseAdapter(BaseAdapter):
    def search_by_keywords(self, keywords, num_results):
        # Implementation
        pass
    # ... implement other methods

# In academic_server.py
from adapters.new_database_adapter import NewDatabaseAdapter

adapters = {
    # ... existing adapters
    "new_database": NewDatabaseAdapter()
}

🎯 Use Cases

For Researchers

Search across multiple preprint servers simultaneously
Find papers by specific authors or topics
Download open access papers automatically
Generate literature review materials
Analyze local PDF collections
Perform comprehensive citation network analysis
Generate automated paper summaries

For AI Assistants

Access comprehensive academic knowledge
Provide up-to-date research information
Help with citation and reference management
Analyze research trends and findings
Process and explain figures from academic papers
Conduct complete research workflows automatically

⚠️ Limitations & Notes

API Rate Limits

PubMed: No API key required, but rate-limited
bioRxiv/medRxiv: No authentication required
arXiv: Rate-limited (1 request per 3 seconds recommended)
Semantic Scholar: Free tier has rate limits; get API key for higher limits at https://www.semanticscholar.org/product/api
Sci-Hub: No authentication required; use responsibly

PDF Availability

PubMed: Only PMC open access articles
bioRxiv/medRxiv: All articles are open access
arXiv: All articles are open access
Semantic Scholar: Depends on publisher policies
Sci-Hub: Wide coverage of academic papers (use for research purposes only)

Local PDF Support

Full text extraction: Extract complete text from local or online PDFs
Figure analysis: List and explain figures from PDF papers
Section parsing: Automatically identify and extract paper sections
Batch processing: Analyze multiple PDFs in a folder simultaneously

Date Formats

PubMed: YYYY/MM/DD
Others: YYYY-MM-DD

🤝 Contributing

Contributions are welcome! Feel free to:

Add new database adapters
Improve existing functionality
Fix bugs
Enhance documentation

📄 License

This project builds upon the PubMed-MCP-Server and follows similar open-source principles.

🙏 Acknowledgments

PubMed-MCP-Server for the original PubMed integration
NCBI E-utilities
bioRxiv/medRxiv API
arXiv API
Semantic Scholar API
Sci-Hub MCP Server (JackKuo666/Sci-Hub-MCP-Server)
FastMCP framework

⚠️ Disclaimer

📊 Project Statistics

Supported Databases: 6 (PubMed, bioRxiv, medRxiv, arXiv, Semantic Scholar, Sci-Hub)
MCP Servers: 2 (academic, academic-research)
Basic MCP Tools: 6
Advanced Research Tools: 15+
Lines of Code: ~3,000
Supported Formats: PDF, metadata, citations, full-text analysis
PDF Support: Both local files and online URLs

🚀 Enhanced Features

Advanced Research Capabilities

Citation Network Analysis: Understand paper relationships and impact
Automated Summarization: Generate summaries in multiple styles
Key Information Extraction: Extract methodology, findings, limitations
Complete Research Workflows: One-click research from topic to summary

PDF Processing

Local and Online Support: Process PDFs from local storage or URLs
Figure Explanation: AI-powered figure analysis and explanation
Section Recognition: Automatic identification of paper sections
Batch Analysis: Process multiple papers simultaneously

Smart Search Features

Concurrent Database Search: Search all databases simultaneously
Intelligent Result Merging: Deduplicate and rank results
Advanced Filtering: Multi-parameter search with date ranges
Source-Specific Optimization: Tailored search for each database

📞 Support

For issues or questions:

Check the documentation above
Review error messages in logs
Ensure all dependencies are installed
Verify your MCP configuration

Happy researching! 📚🔬

Academic MCP Server

Academic MCP Server

🌟 Features

Supported Databases

Core Capabilities

🚀 Quick Start

Prerequisites

Installation

Configuration for Cursor

📖 Usage

Search Papers

Advanced Search

Get Paper Metadata

Download PDF

List Available Sources

Deep Paper Analysis

🛠 MCP Tools Reference

Server: academic (Basic Search & Retrieval)

1. search_papers

2. search_papers_advanced

3. get_paper_metadata

4. download_paper_pdf

5. list_available_sources

6. deep_paper_analysis

Server: academic-research (Advanced Analysis & Research)

1. analyze_citation_network

2. evaluate_paper_impact

3. recommend_related_papers

4. research_workflow_complete

5. analyze_local_paper

6. list_all_figures

7. explain_specific_figure

8. extract_text_from_pdf

9. batch_analyze_local_papers

10. compare_papers

11. extract_key_information

12. generate_paper_summary

13. extract_pdf_fulltext

📊 Standardized Output Format

🔧 Architecture

Dual Server Design

Project Structure

Adapter Pattern

Adding New Databases

🎯 Use Cases

For Researchers

For AI Assistants

⚠️ Limitations & Notes

API Rate Limits

PDF Availability

Local PDF Support

Date Formats

🤝 Contributing

📄 License

🙏 Acknowledgments

⚠️ Disclaimer

📊 Project Statistics

🚀 Enhanced Features

Advanced Research Capabilities

PDF Processing

Smart Search Features

📞 Support

Academic MCP Server

🌟 Features

Supported Databases

Core Capabilities

🚀 Quick Start

Prerequisites

Installation

Configuration for Cursor

📖 Usage

Search Papers

Advanced Search

Get Paper Metadata

Download PDF

List Available Sources

Deep Paper Analysis

🛠 MCP Tools Reference

Server: academic (Basic Search & Retrieval)

1. search_papers

Server: `academic` (Basic Search & Retrieval)

1. `search_papers`

2. `search_papers_advanced`

3. `get_paper_metadata`

4. `download_paper_pdf`

5. `list_available_sources`

6. `deep_paper_analysis`

Server: `academic-research` (Advanced Analysis & Research)

1. `analyze_citation_network`

2. `evaluate_paper_impact`

3. `recommend_related_papers`

4. `research_workflow_complete`

5. `analyze_local_paper`

6. `list_all_figures`

7. `explain_specific_figure`

8. `extract_text_from_pdf`

9. `batch_analyze_local_papers`

10. `compare_papers`

11. `extract_key_information`

12. `generate_paper_summary`

13. `extract_pdf_fulltext`

Server: `academic` (Basic Search & Retrieval)

1. `search_papers`

2. `search_papers_advanced`

3. `get_paper_metadata`

4. `download_paper_pdf`

5. `list_available_sources`

6. `deep_paper_analysis`

Server: `academic-research` (Advanced Analysis & Research)

1. `analyze_citation_network`

2. `evaluate_paper_impact`

3. `recommend_related_papers`

4. `research_workflow_complete`

5. `analyze_local_paper`

6. `list_all_figures`

7. `explain_specific_figure`

8. `extract_text_from_pdf`

9. `batch_analyze_local_papers`

10. `compare_papers`

11. `extract_key_information`

12. `generate_paper_summary`

13. `extract_pdf_fulltext`