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.
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.
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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