arXiv Research Assistant MCP Server
The arXiv Research Assistant MCP Server allows users to interact with the arXiv.org database efficiently, enabling:
Search papers by keywords: Find relevant arXiv papers with options to limit results and sort by relevance
Fetch recent papers by category: Retrieve the latest submissions from specific arXiv categories (e.g., cs.AI)
Get detailed paper info: Access comprehensive metadata for papers using their arXiv ID
Analyze trends: Examine recent trends in specified categories over a customizable time period
Generate summarization prompts: Create prompts to help LLMs summarize selected papers
Generate comparison prompts: Build structured prompts for comparing two papers
Search papers by author: Find publications by specific researchers
Provides tools for searching arXiv papers by keywords, retrieving latest papers by category, fetching detailed paper metadata, searching papers by author, and analyzing trending topics within the arXiv database.
Retrieves DOI information as part of the paper details lookup functionality.
Parses XML responses from the arXiv API to extract paper metadata and other information.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@arXiv Research Assistant MCP Server search for papers about large language models in the last month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
π§ arXiv Research Assistant MCP Server
This project is an MCP (Model Context Protocol) server built to interact with the vast arXiv.org paper database.
It allows clients like Claude AI to search, explore, and compare arXiv papers efficiently β all through a custom-built, local server. Itβs built with Python and the FastMCP framework, and uses uv for lightweight package management.
β¨ Features
π Keyword-based Paper Search
Search arXiv papers by keywords, with options to sort by relevance or most recent.π Latest Papers by Category
Specify an arXiv category code (e.g.,cs.AI,math.AP) to fetch the most recent papers in that field.π Paper Details Lookup
Fetch detailed metadata using a paper's arXiv ID: title, authors, abstract, categories, DOI, PDF link, and more.π§βπ¬ Author-based Paper Search
Retrieve a list of papers published by a specific author.π Trend Analysis (Experimental)
Get an overview of trending keywords or topics based on recent papers in a category (currently uses mock data).π Summarization Prompt Generator
Dynamically generate prompts that help LLMs summarize a selected paper more effectively.π Comparison Prompt Generator
Provide two paper IDs to generate a structured prompt for comparing their content.
Related MCP server: scholar-search-mcp
π οΈ Tech Stack
Python 3.11+
uv (for dependency & environment management)
requests (for API communication)
xml.etree.ElementTree (for parsing XML responses)
π Getting Started
Installing via Smithery
To install arXiv Research Assistant MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install arxiv-paper-mcp --client claudeInstallation from PyPI
uv pip install arxiv-paper-mcpπ§ Clone the repository (for development)
git clone https://github.com/daheepk/arxiv-mcp-server.git
cd arxiv-mcp-serverπ§ Install Dependencies (for development)
Use uv to install all dependencies in editable mode:
uv pip install -e .βοΈ How to Run
βΆοΈ Run the server (locally)
arxiv-paper-mcpπ Use with Claude
To use this MCP server with Claude, add the following JSON configuration to Claude's MCP settings:
{
"mcpServers": {
"arXivPaper": {
"command": "uv",
"args": [
"tool",
"run",
"arxiv-paper-mcp"
]
}
}
}Project Structure
arxiv-mcp-server/
βββ arxiv_mcp/ # Main package
β βββ __init__.py
β βββ app.py # FastMCP app setup
β βββ server.py # Server entry point
β βββ utils.py # arXiv API communication logic
β βββ resources/ # MCP resources (categories, authors, etc.)
β βββ tools/ # MCP tools (search, detail lookup, trends)
β βββ prompts/ # Prompt templates (summarize, compare)
βββ pyproject.toml # Project config & dependencies
βββ README.md # This fileAvailable Tools
4 toolsanalyze_trendsC
νΉμ μΉ΄ν κ³ λ¦¬μ μ΅μ νΈλ λλ₯Ό λΆμν©λλ€.
Inputs: category, days.
get_paper_infoC
λ Όλ¬Έ IDλ‘ μμΈ μ 보λ₯Ό κ°μ Έμ΅λλ€.
Inputs: paper_id (required).
scrape_recent_category_papersC
[ν¬λ‘€λ§] νΉμ μΉ΄ν κ³ λ¦¬μ 'recent' νμ΄μ§λ₯Ό μ€ν¬λ©νμ¬ μ΅μ λ Όλ¬Έ λͺ©λ‘μ κ°μ Έμ΅λλ€.
Inputs: category (required), max_results.
search_papersC
ν€μλλ‘ arXiv λ Όλ¬Έμ κ²μν©λλ€.
Inputs: keyword (required), max_results.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogβ¦
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: analyze_trends focuses on trends analysis, get_paper_info retrieves detailed paper information, scrape_recent_category_papers scrapes recent papers from a category, and search_papers searches by keywords. There is no overlap or ambiguity in their functions.
The tool names follow a consistent snake_case pattern and use descriptive verbs (analyze, get, scrape, search). However, scrape_recent_category_papers includes a Korean annotation '[ν¬λ‘€λ§]' in its description, which slightly deviates from the pure English naming convention, though this does not affect the tool name itself.
With 4 tools, the server is well-scoped for an arXiv research assistant, covering key operations like searching, scraping, analyzing trends, and fetching details. It is slightly lean but reasonable, as it handles core research workflows without unnecessary bloat.
The toolset covers essential functions for arXiv research, including search, scrape, analysis, and info retrieval. However, there are minor gaps such as no tools for filtering or sorting results, managing user preferences, or accessing advanced arXiv features like citation tracking, which could limit agent capabilities in complex scenarios.
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