Scholar MCP Server

Scholar MCP Server

A local academic tool that enables searching across nine academic sources, downloading PDFs, and performing AI-powered analysis of research papers. It also supports generating citation networks and recommending papers based on local workspace code.

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README

Scholar MCP Server

Local academic paper tool MCP server — 9-source search, multi-source download, AI-powered analysis, citation graph, code-based paper recommendation.

PyPI Python Tests License

Quick Install

pip install scholar-mcp-server[all]
scholar-mcp-install --all

That's it. Restart your IDE and start using it.

Features

Tool Description
paper_search 9-source concurrent search with relevance scoring (Semantic Scholar, OpenAlex, Crossref, PubMed, arXiv, CORE, Europe PMC, DOAJ, dblp)
paper_download Multi-source PDF download: Unpaywall → Publisher OA → arXiv → Sci-Hub → scidownl
paper_batch_download Batch download multiple papers by DOI list
paper_ai_analyze AI analysis — downloads PDF, extracts full text (up to 20 pages / 12k chars), sends to any OpenAI-compatible API
paper_recommend Scan your workspace code → multi-query auto-recommend related papers
paper_citation_graph Generate Mermaid citation/reference network visualization
paper_health Check download source availability

Search Quality

Search results are ranked by a 4-factor composite score:

Factor Weight Description
Query relevance 0–40 Title + abstract term matching
Citation impact 0–30 Log-scaled citation count
Source quality 0–10 Data source reliability weighting
Year recency 0–15 Boost for recent publications

Deduplication uses DOI matching + Jaccard title similarity (≥0.7 threshold) across all 9 sources. Each source connector has built-in retry with exponential backoff.

AI Analysis

paper_ai_analyze works with any OpenAI-compatible API. Set AI_API_BASE, AI_API_KEY, and AI_MODEL to point to your preferred provider.

Alternative Install (Git Clone)

git clone https://github.com/45645678a/scholar-mcp.git
cd scholar-mcp
pip install -r requirements.txt
python install.py --all

Environment Variables

Variable Description Required
AI_API_KEY API key for AI analysis For paper_ai_analyze
AI_API_BASE API base URL (any OpenAI-compatible endpoint) Optional (default: https://api.deepseek.com)
AI_MODEL Model name Optional (default: deepseek-chat)
UNPAYWALL_EMAIL Email for Unpaywall API Optional

Supported IDEs

  • Antigravity (Gemini)
  • Cursor
  • Windsurf
  • Claude Code / Claude Desktop
  • VS Code (Copilot)

Search Sources (9)

All free, no API keys required:

Source Coverage
Semantic Scholar Broad academic (primary)
OpenAlex 250M+ works, global
Crossref DOI metadata
PubMed Biomedical
arXiv Physics, CS, Math
CORE Open Access aggregator
Europe PMC European biomedical
DOAJ Open Access journals
dblp Computer Science

Development

pip install .[all] pytest
pytest tests/ -v

40 tests covering search dedup, download chain, keyword extraction, and connector mocking.

⚠️ Disclaimer

This tool includes optional Sci-Hub integration for personal academic use. Sci-Hub may be illegal in some jurisdictions. Users are solely responsible for ensuring compliance with local laws and institutional policies. The authors do not endorse copyright infringement. If you are in a compliance-sensitive environment (university, company, lab), consult your institution's policy before using the Sci-Hub download source.

License

MIT

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