# Semantic Scholar MCP

> Semantic Scholar MCP connects your AI agent to a knowledge graph containing over 200 million academic papers. Instantly search across all STEM fields, getting single-sentence summaries of complex research. Track how ideas evolve by finding influential citations and building detailed researcher profiles with metrics like the h-index.

## Overview
- **Category:** the-unthinkable
- **Price:** Free
- **Tags:** academic-research, citation-analysis, knowledge-graph, literature-review, scientific-data, research-profiles, summarization, stem

## Description

Need to dive into deep academic literature? This MCP gives your AI client access to the world's largest knowledge graph of scholarly papers. You can search across 200M+ works, getting instant, single-sentence summaries that distill the core insight from any paper—a massive time saver compared to reading abstracts.

It goes beyond simple searches. You track true influence by seeing which citations meaningfully build upon a work, not just how many people mentioned it. Whether you're analyzing career trajectories or building a literature review, you can look up papers using their DOI, ArXiv ID, or PubMed ID. The Vinkius catalog makes this data available to any compatible client, letting your agent treat academic research like structured data. You find researchers by name and immediately pull up metrics, including paper counts and the h-index, giving you a clear picture of who's leading in their field.

## Tools

### get_semantic_citations
Finds all the papers that cite a specific work for impact analysis.

### get_semantic_paper
Retrieves full paper details using its Semantic Scholar ID, DOI, ArXiv ID, or PMID.

### search_semantic_author
Returns a researcher's metrics, including their total papers and h-index score.

### search_semantic_scholar
Searches 200M+ academic papers for summaries, citation counts, and fields of study.

## Prompt Examples

**Prompt:** 
```
What are the most cited papers on transformer architecture in deep learning?
```

**Response:** 
```
Top results include: 'Attention Is All You Need' by Vaswani et al. (2017) — 🤖 TLDR: The Transformer, a new network architecture based solely on attention mechanisms, achieves superior quality on machine translation tasks. Cited 120,000+ times (25,000+ influential). Fields: Computer Science, Linguistics.
```

**Prompt:** 
```
Get the full details of the LoRA paper using its ArXiv ID arXiv:2106.09685.
```

**Response:** 
```
Found: 'LoRA: Low-Rank Adaptation of Large Language Models' by Edward Hu et al. (2021). 🤖 TLDR: LoRA freezes the pre-trained model weights and injects trainable rank decomposition matrices, reducing trainable parameters by 10,000x and GPU memory by 3x. Citations: 8,500+ (2,100 influential). Fields: CS, Mathematics. Available at arxiv.org/abs/2106.09685.
```

**Prompt:** 
```
Find the researcher Yann LeCun and show me his publication metrics.
```

**Response:** 
```
Found researcher Yann LeCun — Author at Meta AI / NYU. Papers: 950+, Total Citations: 380,000+, h-index: 162. Known for pioneering work in convolutional neural networks, self-supervised learning, and modern AI architectures. Profile link and full publication list available.
```

## Capabilities

### Search vast academic databases
Find papers across 200M+ works using AI-generated summaries and filtering by fields like Computer Science or Medicine.

### Analyze research influence chains
Determine which other papers cite a specific work, allowing you to trace how an academic idea evolved over time.

### Retrieve full paper details by ID
Get complete metadata for any article using its Semantic Scholar ID, DOI, ArXiv ID, or PMID.

### Build researcher profiles and metrics
Look up academics to see their total publication count, citation history, and h-index score.

## Use Cases

### Mapping an evolving technology field
A researcher wants to see how 'Transformer' architectures evolved. They ask their agent to use get_semantic_citations on the foundational paper, allowing them to trace every subsequent development and key breakthrough in the field.

### Evaluating a potential collaborator
A hiring manager needs to vet a candidate's research depth. They ask their agent to run search_semantic_author on the candidate’s name, instantly retrieving their h-index and total citation count for comparison.

### Deep literature review prep
A PhD student needs papers on quantum computing using various identifiers. They use get_semantic_paper, feeding it a mix of DOI and ArXiv IDs to ensure they capture every relevant article in the initial search.

### Finding niche scientific breakthroughs
An R&D team needs papers on rare diseases. They run search_semantic_scholar, filtering by Medicine or Biology, getting AI summaries that quickly surface only the most promising, relevant studies.

## Benefits

- Skip the abstract reading. Instead of wasting time skimming lengthy paper abstracts, use the main search function to get an AI-generated TLDR summary for every result immediately.
- Track true academic impact. Don't just count citations; analyze influential citations to see which works truly advanced a field, giving you a deeper understanding of research importance.
- Eliminate ID guesswork. Never manually look up details again. You can fetch any paper using its DOI, ArXiv ID, or PMID with the get_semantic_paper tool.
- Build talent profiles instantly. Instead of piecing together career data from different university sites, use search_semantic_author to pull a comprehensive h-index and citation count for any scholar.
- Map idea evolution. Use the get_semantic_citations tool to build a precise map of how a core concept was cited and refined by subsequent works.

## How It Works

The bottom line is that this MCP turns massive, unstructured academic archives into actionable, queryable datasets.

1. Connect your AI client to this MCP in the Vinkius Marketplace.
2. Instruct your agent what you're looking for: a specific topic, an author, or an existing paper ID.
3. Your agent executes the necessary tool call and returns structured data, including summaries and related metrics.

## Frequently Asked Questions

**How do I use semantic-scholar to find a specific paper?**
You can use get_semantic_paper. You don't need the title; just provide any unique identifier like its DOI, ArXiv ID (e.g., arXiv:2106.09685), or PMID.

**Can semantic-scholar track which papers are most influential?**
Yes. The main search function provides 'influential citation counts.' This is better than a simple count because it measures how meaningfully a paper builds on prior work.

**What data does the search_semantic_author tool provide?**
It gives you a clear profile of an academic, including their total number of papers published, their overall citation count, and their career impact metric (h-index).

**Is semantic-scholar useful for general topics or just AI/ML?**
It covers all STEM fields—Computer Science, Medicine, Biology, Physics, and more. While it's strong in AI/ML, its scope is vast.

**Can I find out what papers cited an old classic paper using get_semantic_citations?**
Absolutely. You provide the ID of the 'classic paper,' and the tool returns a list of all newer works that have built upon it, forming an influence chain.