45645678a

45645678a / Scholar-mcp

Public

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

18
0
69% credibility
Found Apr 13, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python tool that integrates into AI-assisted code editors to enable searching across multiple academic sources, downloading papers, AI-powered analysis, code-based recommendations, citation graphs, and translation.

How It Works

1
📰 Discover the Paper Helper

You hear about a handy tool that finds research papers super fast right inside your coding app, making studying easier.

2
📦 Add It Easily

You grab it with one quick install command, like downloading a useful app on your phone.

3
🔧 Link to Your Coding Space

You tell your AI coding helper to use this new friend, and it sets everything up automatically.

4
🔍 Search for Papers

You type what you're curious about, and it pulls the best matches from lots of free research spots, ranked by importance.

5
📥 Grab and Explore Papers

Pick a paper, download its full version if available, or let smart AI summarize its key ideas and findings.

6
💡 Get Smart Recommendations

It scans your own notes or code, suggests related papers, draws connection maps, and even translates if needed.

🎉 Research Supercharged

Now your projects get backed by top papers instantly, saving hours and sparking great ideas effortlessly.

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Star Growth

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AI-Generated Review

What is Scholar-mcp?

Scholar-mcp is a Python-based MCP server that turns your AI IDE into a local academic paper powerhouse, searching nine free sources like Semantic Scholar and arXiv, downloading PDFs from Unpaywall to Sci-Hub, and offering AI analysis, translation, citation graphs, and code-scanning recommendations. It's a github local alternative to Google Scholar mcp github, solving the hassle of tab-switching for legal academic papers during coding or research. Install via pip, run the one-liner setup, and tools like `paper_search` or `paper_ai_analyze` appear in Cursor, VS Code Copilot, or Claude.

Why is it gaining traction?

It stands out with concurrent 9-source searches ranked by relevance, citations, and recency, plus fallback downloads that actually work, unlike flaky browser extensions. The hook? Workspace code analysis auto-recommends papers based on your imports (e.g., PyTorch triggers deep learning hits), and Mermaid citation graphs paste straight into notes. As a local github copilot alternative for local academic databases, it runs offline-capable with any OpenAI-compatible API.

Who should use this?

PhD students in ML or physics prototyping models, who scan code for "transformer" and want instant paper recs. Hardware devs optimizing coils (cpp mcphee scholar vibes) needing magnetic field papers without Google Scholar rabbit holes. Academic devs at local academic journals or labs evaluating local academic performance via citation graphs.

Verdict

Grab it if you're in an AI IDE and tired of PDF hunts—solid 40 tests, PyPI-ready, but 18 stars and 0.7% credibility score scream early beta; test downloads first. Worth the pip for daily research flows.

(187 words)

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