MCP.so
登录

AI - Research Assistant MCP Server

@hamid-vakilzadeh

基本信息

分类

数据与分析

传输方式

stdio

作者

hamid-vakilzadeh

关于 AI - Research Assistant MCP Server

Semantic Scholar MCP

AI - Research Assistant MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块,请前往代码仓库查看安装说明。

代码仓库

AI - Research Assistant MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is AI - Research Assistant?

AI - Research Assistant is a Model Context Protocol (MCP) server that provides AI models with comprehensive access to the Semantic Scholar Academic Graph API. It enables intelligent literature search, paper analysis, and citation network exploration through a set of tools, resources, and prompts. This server is intended for researchers and developers who want to integrate academic research capabilities into AI‑powered workflows.

How to use AI - Research Assistant?

Install Node.js, then install the MCP server and restart your AI platform (e.g., Claude Desktop). Obtain a Semantic Scholar API key (optional but recommended for higher rate limits) and set it as the environment variable SEMANTIC_SCHOLAR_API_KEY. Finally, deploy using your preferred MCP client or integration platform.

Key features of AI - Research Assistant

  • Comprehensive paper search (basic, advanced, title matching, batch retrieval)
  • Author discovery and analysis (profiles, h-index, publication lists)
  • Citation network analysis (citing papers, references, multi-depth traversal)
  • Field‑specific research (top papers by discipline, venue filtering, open access)
  • Rate‑limited API access with throttling (10 req/sec standard, 1 req/sec for batch operations)

Use cases of AI - Research Assistant

  • Literature reviews: discover and analyze research trends across academic fields
  • Citation analysis: track research impact and identify influential papers
  • Research discovery: find relevant papers, authors, and venues for ongoing work
  • Academic network analysis: explore collaboration patterns and citation relationships
  • Gap analysis: identify underexplored research areas and opportunities

常见问题

1

What are the Semantic Scholar API rate limits?

The API allows up to 100 requests per 5 minutes. To access a higher rate limit, visit Semantic Scholar to request authentication for your project.

2

What tools does AI - Research Assistant provide?

Tools include `papers-search-basic`, `papers-search-advanced`, `papers-match`, `papers-get`, `papers-batch`, `papers-citations`, `papers-references`, `authors-search`, `authors-papers`, and `analysis-citation-network`.

3

Are there pre‑configured prompts?

Yes, three prompts are available: `literature-review` (systematic review with trend analysis), `citation-analysis` (impact assessment), and `research-gap-finder` (identify unexplored opportunities).

4

How does this MCP relate to the research paper mentioned?

The MCP extends the work of an academic paper on using retrieval‑augmented generation (RAG) as a research assistant. While MCP is not covered in the paper, it continues the same effort and reflects lessons learned.

5

What runtime dependencies are required?

The server requires Node.js to be installed. An optional Semantic Scholar API key can be set via the environment variable `SEMANTIC_SCHOLAR_API_KEY` to obtain higher rate limits.

评论

数据与分析 分类下的更多 MCP 服务器