scholar-search-mcp

Name: scholar-search-mcp
Author: Silung

Verified

An MCP server for academic paper search that integrates with AI assistants (e.g., Claude Code, Cursor), enabling them to search and retrieve academic paper metadata.

210stars

4forks

Python

Installation

# Add to your Claude Code skills
git clone https://github.com/Silung/scholar-search-mcp

Getting Started

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Security ReportVerified

Last scanned: 5/30/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-30T15:43:44.113Z",
  "npmAuditRan": true,
  "pipAuditRan": false
}

README.md

Frequently Asked Questions

What is scholar-search-mcp?

scholar-search-mcp is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Silung. An MCP server for academic paper search that integrates with AI assistants (e.g., Claude Code, Cursor), enabling them to search and retrieve academic paper metadata. It has 210 GitHub stars.

Is scholar-search-mcp safe to use?

Yes. scholar-search-mcp passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install scholar-search-mcp?

Clone the repository with "git clone https://github.com/Silung/scholar-search-mcp" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is scholar-search-mcp written in?

scholar-search-mcp is primarily written in Python. It is open-source under Silung on GitHub, so you can review or fork the full source.

Are there alternatives to scholar-search-mcp?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh scholar-search-mcp against similar tools.

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Scholar Search MCP

An MCP server for academic literature workflows in Claude, Cursor, and other MCP clients.

It combines Semantic Scholar and arXiv into one unified toolset, with fast parallel search, normalized outputs, source-aware deduplication, and practical research utilities (citations, references, author graph, recommendations, and arXiv source download).

Why this project
Demo videos
Install
Quick setup (Claude Desktop / Cursor)
Environment variables
Tool list
Testing with MCP Inspector
Contributing

Why this project

Most paper tools force you to choose one source or one API style. scholar-search-mcp provides one MCP layer for literature search and graph retrieval:

One MCP server, multiple scholarly sources
Free-first defaults (arXiv works without keys)
LLM-friendly outputs for downstream reasoning and agent workflows
Practical research actions, not only search
Unified search: search_papers runs Semantic Scholar + arXiv in parallel and deduplicates by normalized title.
Research graph tools: details, citations, references, author profile/papers, and recommendations.
Batch + source workflows: fetch up to 500 papers, and download/extract arXiv LaTeX sources.
Operational controls: built-in caching plus env-based source toggles (enable/disable channels).
Source strategy: built-in Semantic Scholar + arXiv, free-first by default (arXiv key-free), optional API key for higher Semantic Scholar limits.

Demo videos

Agent writes a survey paper with Scholar Search MCP.

Install

pip install scholar-search-mcp

Requires Python 3.10+.

Quick setup (Claude Desktop / Cursor)

Use the same server command in both clients:

{
  "mcpServers": {
    "scholar-search": {
      "command": "python",
      "args": ["-m", "scholar_search_mcp"],
      "env": {
        "SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR": "true",
        "SCHOLAR_SEARCH_ENABLE_ARXIV": "true"
      }
    }
  }
}

SEMANTIC_SCHOLAR_API_KEY is optional. Add it only if you want higher Semantic Scholar rate limits:

{
  "mcpServers": {
    "scholar-search": {
      "command": "python",
      "args": ["-m", "scholar_search_mcp"],
      "env": {
        "SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR": "true",
        "SCHOLAR_SEARCH_ENABLE_ARXIV": "true",
        "SEMANTIC_SCHOLAR_API_KEY": "your-key"
      }
    }
  }
}

Difference:

Claude Desktop: edit local config file directly.
Cursor: add an MCP server in Cursor settings UI (or corresponding settings JSON).

Claude Desktop config file locations:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

Environment variables

Variable	Description
`SEMANTIC_SCHOLAR_API_KEY`	Optional. Increases Semantic Scholar rate limits.
`SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR`	`true/false`, default `true`.
`SCHOLAR_SEARCH_ENABLE_ARXIV`	`true/false`, default `true`.
`SCHOLAR_SEARCH_CACHE_DIR`	Optional cache directory path.
`SCHOLAR_SEARCH_CACHE_TTL_SECONDS`	Cache TTL in seconds, default `86400`.
`SCHOLAR_ARXIV_SOURCE_DIR`	Default parent directory for extracted arXiv sources.

Example (arXiv only):

{
  "SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR": "false",
  "SCHOLAR_SEARCH_ENABLE_ARXIV": "true"
}

Tool list

Tool	Purpose
`search_papers`	Search papers with optional `limit`, `fields`, `year`, `venue`.
`get_paper_details`	Get one paper by DOI, arXiv ID, S2 ID, or URL.
`get_paper_citations`	Get papers that cite a given paper.
`get_paper_references`	Get references of a given paper.
`get_author_info`	Get an author profile by ID.
`get_author_papers`	Get papers by a given author.
`get_paper_recommendations`	Get similar paper recommendations.
`batch_get_papers`	Batch fetch paper details (up to 500 IDs).
`download_arxiv_source`	Download and extract arXiv source bundle (`tar.gz`).

Testing with MCP Inspector

npm install -g @modelcontextprotocol/inspector
mcp-inspector python -m scholar_search_mcp

Contributing

Issues and PRs are welcome: fork repo, create branch, add validation/tests, and open a PR with clear before/after behavior.