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AI - Research Assistant MCP Server

@hamid-vakilzadeh

Repository

Basic information

Category

Data & Analytics

Transports

stdio

Author

hamid-vakilzadeh

About AI - Research Assistant MCP Server

Semantic Scholar MCP

#mcp#mcp-server

AI - Research Assistant MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

AI - Research Assistant MCP Server tools

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Overview

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

Frequently asked questions

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.

Comments

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