MCP 服务器

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.