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AI Research Assistant

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hamid-vakilzadeh · Research

38.5k installs

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The AI Research Assistant MCP server gives any AI agent direct, structured access to academic literature through Semantic Scholar and arXiv, turning scattered scholarly search into a single callable interface. Instead of manually hunting through databases or copy-pasting DOIs, an agent connected through BusinessMCP's unified endpoint can search millions of papers, pull citation graphs, and extract full-text PDF content on demand — all without leaving the conversation. This makes it a natural fit for teams building research copilots, literature-review automations, or internal knowledge tools that need to ground answers in peer-reviewed sources rather than generic web results.

What sets this server apart from a general-purpose search tool is its academic-specific structure: it understands citations, references, author metadata, and paper versions (including arXiv preprints), which lets an agent answer questions like "what papers cite this study" or "summarize the methodology section of this PDF" with far more precision than a keyword search engine could. Because it's hosted through BusinessMCP.com, this Semantic Scholar and arXiv access sits alongside your other business tools, databases, and ad platforms in one unified MCP server — connect it once at /api/mcp and it's immediately available to Claude, GPT, Gemini, or any model-agnostic agent your team runs, with usage and activity visible in the same business-intelligence dashboard as everything else.

Teams typically reach for this MCP server when they need reliable, citable academic grounding: due-diligence teams verifying scientific claims, product teams tracking research trends in a technical field, R&D groups doing literature reviews, or content teams fact-checking claims against peer-reviewed sources. Full-text PDF extraction means an agent isn't limited to abstracts — it can pull actual findings, tables, and discussion sections to answer nuanced questions, while citation analysis tools help surface influential papers and track how ideas propagate across a field over time.

Because BusinessMCP is cookieless and GDPR-friendly by design, research queries routed through this server avoid the tracking baggage that comes with many commercial search APIs, which matters for compliance-conscious organizations doing competitive or scientific research. And since the server is exposed identically whether you're working inside BusinessMCP's cloud growth-suite app or calling your own Bearer-authenticated endpoint, engineering teams can prototype research agents quickly and then wire the exact same tool calls into production pipelines without re-architecting anything.

Paired with general web search or fact-checking tools in the same hosted MCP instance, the AI Research Assistant becomes one layer in a broader research stack — web search for current events and news, academic search for peer-reviewed depth, and a shared BI dashboard to see which queries and sources your agents are actually relying on. For any organization whose agents need to cite real papers rather than hallucinate references, this is the server that makes academic search a first-class, governable capability rather than a bolt-on script.

$ npx mcphosting-cli add hamid-vakilzadeh-mcpsemanticscholar

Just say it in a thread

No configs, no docs. Once connected, these are the kinds of messages your agents act on.

"Search semantic scholar and arxiv for papers matching a query, topic, or author — and give me the highlights."

"Retrieve metadata, abstract, and links for a specific paper by id or doi for me, then post a summary in the thread."

"List papers that cite a given paper to trace its influence and downstream research and flag anything that needs my approval."

What teams use it for

  • Automating literature reviews by searching Semantic Scholar and arXiv for relevant peer-reviewed papers on a topic
  • Verifying scientific claims in marketing or content copy by pulling and citing the original research
  • Tracking citation networks to identify influential papers and emerging research trends in a field
  • Extracting full-text sections from PDFs to answer detailed methodology or results questions without manual reading
  • Building an internal R&D research copilot that cites real academic sources instead of hallucinated references

Agent-callable tools

search_papers

Search Semantic Scholar and arXiv for papers matching a query, topic, or author.

get_paper_details

Retrieve metadata, abstract, and links for a specific paper by ID or DOI.

get_citations

List papers that cite a given paper to trace its influence and downstream research.

get_references

Retrieve the reference list of a paper to explore its foundational sources.

extract_pdf_text

Extract full-text content from a paper's PDF for detailed section-level analysis.

search_by_author

Find papers published by a specific author across Semantic Scholar and arXiv.

get_arxiv_preprint

Fetch the latest arXiv preprint version and metadata for a given paper.

Your data stays yours

Credentials live in your vault. We route requests — we never store, log, or train on your data.

Works with every AI

Connect once — portable across Claude, GPT, Gemini, and every local agent you run.

Frequently asked questions

Does this MCP server only cover Semantic Scholar, or also arXiv preprints?

It covers both — you can search published, peer-reviewed papers indexed by Semantic Scholar as well as arXiv preprints, giving agents access to both established and cutting-edge research.

Can it read the actual content of a paper, not just the abstract?

Yes, the server supports full-text PDF extraction so an agent can pull specific sections like methodology or results rather than relying on abstracts alone.

How does this fit into BusinessMCP's unified MCP setup?

Once connected, it's exposed through your single hosted MCP endpoint at /api/mcp alongside your other tools and data sources, so any AI agent can call it and its usage shows up in your shared business-intelligence dashboard.

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