# Semantic Scholar

> Use this tool when you need to search and analyze academic research papers, authors, and citations to inform literature reviews, research workflows, or AI-powered research assistants. It provides comprehensive access to paper and author information, citation networks, and paper recommendations through a robust API interface. Ideal for academic research, citation analysis, and building AI research assistants, it accepts inputs such as search queries, author names, and paper titles, and outputs detailed paper information, citations, and recommendations.

Canonical page: https://skillsregistry.net/skills/alperenkocyigit-semantic-scholar  
JSON: https://api.skillsregistry.net/v1/skills/alperenkocyigit-semantic-scholar

## Description

This MCP server provides comprehensive access to the Semantic Scholar academic search API, enabling AI assistants to search for research papers, retrieve detailed paper and author information, analyze citation networks, and discover paper recommendations. Built by Alperen Kocyigit using Python with FastMCP, it implements robust retry logic with exponential backoff for rate limiting and includes advanced features like batch operations for multiple papers/authors, text snippet search within papers, autocomplete suggestions, and ML-powered paper recommendations based on positive/negative examples. The implementation is designed for academic research workflows, literature reviews, citation analysis, and building AI research assistants that need to navigate and analyze the scholarly literature landscape.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-08-17

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/alperenkocyigit-semantic-scholar)
- **Repository:** <https://github.com/alperenkocyigit/semantic-scholar-graph-api>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alperenkocyigit-semantic-scholar"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alperenkocyigit-semantic-scholar` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alperenkocyigit-semantic-scholar/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt