Academic Research MCP Server
Research papers from arXiv, Google Scholar, and Wikipedia with citation metrics
š Research MCP ā AI Paper Search & Citations
Pricing
from $10.00 / 1,000 results
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š Research MCP ā AI Paper Search & Citations
nexgendata/academic-research-mcp-server
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š Academic research, scholarly papers, arXiv, PubMed, Google Scholar MCP server for AI agents (Claude Desktop, Cursor, OpenAI Agents SDK). Search papers + citations + authors + abstracts via MCP ā built for literature-review and research AI workflows. Free tier available.
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Stephan Corbeil
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Bookmarked
11
Total users
6
Monthly active users
5 days ago
Last modified
Categories
AI
Developer tools
MCP servers
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Pricing
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š Academic Research MCP Server ā arXiv, Google Scholar & OpenAlex for Claude / ChatGPT
Connect AI agents to live academic-research data through the Model Context Protocol ā arXiv papers, Google Scholar citations, OpenAlex metadata, and research-trend signal. A drop-in alternative to the rate-limited Semantic Scholar API, Connected Papers, Scite, and direct OpenAlex queries.
Why This MCP Server Beats Semantic Scholar API, Connected Papers, OpenAlex & Scite
| Feature | NexGenData Academic Research MCP | Semantic Scholar API | Connected Papers API | OpenAlex direct | Scite |
|---|---|---|---|---|---|
| Cost | Pay-per-event, ~$0.003 per tool call | Free, heavy rate-limit | Free UI, paid API | Free, query-cap | $20-50 / user / month |
| AI agent integration | Native MCP ā Claude Desktop, Cursor | None ā REST only | None ā UI only | None | None |
| Rate limit | Apify-managed | 100 req / 5 min | Strict | 100,000 / day cap | Per-seat |
| Coverage | arXiv + Scholar + OpenAlex + NIH | Open papers only | Visual graph | Open papers | Citation context |
| Time-to-first-call | < 60 seconds | API-key signup | Account + plan | API key | Per-seat license |
| Auth | Apify token | API key + tier | Plan + API key | Email + key | Institution / seat |
| Output | Structured JSON for LLM function-calls | JSON | Visual graph | JSON | JSON |
Most research teams pick this MCP server because it is the only way to give an AI agent unified access to four academic-data sources at once ā without juggling separate rate-limited keys.
What You Get
Tools exposed to your AI agent:
search_papersā keyword / author / field search across arXiv + Scholar + OpenAlexget_paper_detailsā abstract, authors, year, citation count, references, DOIfind_related_papersā co-citation + bibliographic-coupling graph traversalget_author_profileā h-index, total citations, paper list, affiliation historycitation_trendsā citations-per-year curve for a paper or authorget_grant_dataā NIH RePORTER grant lookup by PI, institution, project numbersubject_trendsā paper-volume growth per field / keyword over time
Each response is clean JSON tuned for LLM tool-use ā no PDF parsing, no XML feed pagination.
Use Cases
- AI research assistants ā Claude pulls top 20 papers on a topic, ranks by recency + citation count, summarizes
- Lit-review copilots ā multi-step agents draft a literature review by chaining
search_papersāfind_related_papersāget_paper_details - Grant-discovery agents ā match a research lab's recent papers to NIH RePORTER grant calls
- Competitive research intel ā track papers from a competitor's R&D group over time
- Academic publishers ā automate "papers citing this paper" alerts via scheduled MCP calls
- VC due-diligence ā find lead inventors behind a deep-tech startup before a meeting
- Policy think-tanks ā surface citation-network influence of competing research camps
Quick Start
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/academic-research-mcp-server").call(run_input={
"tool": "search_papers",
"params": {"query": "retrieval augmented generation", "year_min": 2024, "limit": 25}
})
for paper in client.dataset(run["defaultDatasetId"]).iterate_items():
print(paper["title"], paper["citations"])
Or connect Claude Desktop / Cursor / Cline directly to the MCP endpoint and ask in natural language: "Find me the top 10 most-cited 2025 papers on diffusion models and summarize the methodology differences."
Pricing
Pay-per-event ā you only pay for tool calls that return data:
- Actor Start: ~$0.0002
- Tool call: $0.003 per call
A typical lit-review agent issues 30-80 tool calls per task ā $0.10-$0.25 total.
Related NexGenData Actors
| Use case | Actor |
|---|---|
| Google Scholar bulk paper + citation scraper | Google Scholar Scraper |
| arXiv keyword search + bulk paper export | arXiv Scraper |
| NIH RePORTER grant + funding lookup | NIH RePORTER Grants Scraper |
| News + press release monitoring for AI agents | News MCP Server |
| Finance + market data for AI agents | Finance MCP Server |
| Developer ecosystem data for AI agents | Developer Tools MCP Server |
| Wikipedia structured-knowledge scraper | Wikipedia Scraper |
| IRS 990 nonprofit financial filings | IRS 990 Nonprofit Explorer |
FAQ
**Q: How does this compare to Semantic Scholar's API?**Semantic Scholar's free API caps at 100 req / 5 minutes and rejects bulk pulls. This MCP server multiplexes across four sources with Apify-managed rate limits so an agent can run a 50-call lit-review uninterrupted.
**Q: Can the agent call this from Claude Desktop?**Yes ā point Claude Desktop's MCP config at the Apify endpoint URL for this actor. Tool discovery is automatic.
**Q: Are paywalled papers included?**Metadata only ā title, abstract, authors, citations. Full-text PDFs require institutional access through the publisher.
**Q: How fresh is the data?**arXiv is refreshed daily, Scholar and OpenAlex hourly, NIH RePORTER weekly.
**Q: What about citation graphs?**Use find_related_papers ā returns co-citation neighbors plus bibliographic-coupling matches.
**Q: Can I get DOIs for cross-referencing?**Yes ā every paper response includes DOI where one exists.
**Q: Is this safe for production research products?**Yes ā runs on Apify auto-scaling infrastructure with logging + retry semantics built in.
About NexGenData
NexGenData publishes 260+ buyer-intent actors plus a family of MCP servers for AI agent workflows (finance, news, sports, real-estate, developer tools, academic research, premium data). All pay-per-result. Browse the full catalog at https://apify.com/nexgendata?fpr=2ayu9b
How NexGenData Pricing Works
Every NexGenData actor uses pay-per-event pricing ā you only pay for results that actually land in your dataset. No monthly minimum, no seat fees, no surprise overage bills.
- Actor Start: a single-event charge each time you spin the actor up (scaled to memory size)
- Result: charged per item written to the default dataset
- No charge for retries, internal proxy rotation, or failed sub-requests ā those are absorbed by the platform
If you only need the data once a quarter, you only pay once a quarter. If you scale to millions of records, the unit cost stays the same.
Apify Platform Bonus
New to Apify? Sign up with the NexGenData referral link ā you get free platform credits on signup (enough for several thousand free results) and you help fund the maintenance of this actor fleet.
Integration Surface
Every actor in the NexGenData catalog can be triggered from:
- Apify console ā point-and-click run
- Apify API ā REST + webhooks
- Apify Python / JS SDKs ā programmatic batch
- Zapier, Make.com, n8n ā official integrations
- MCP ā many actors are exposed as MCP tools for Claude / ChatGPT / Cursor agents
- Schedules ā built-in cron for daily / weekly / monthly runs
- Webhooks ā POST results to any HTTPS endpoint on dataset write
Support
NexGenData maintains 260+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get a response within 24 hours. Roadmap requests are welcome ā high-demand features ship in the next version.
š Home: thenextgennexus.com š¦ Full catalog: apify.com/nexgendata
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