Academic Research MCP Server
arXiv、Google Scholar、Wikipediaからの研究論文と引用指標
ドキュメント
Academic Research MCP — arXiv & Scholar for AI Agents (nexgendata/academic-research-mcp-server) Actor
MCP server exposing arXiv and OpenAlex/Crossref paper search (an open Google Scholar alternative — Scholar itself is not queried) as agent tools: titles, authors, citation counts, DOIs, links. Connect Claude, Cursor, n8n or the OpenAI Agents SDK. $0.02 per successful tool call.
- URL: https://apify.com/nexgendata/academic-research-mcp-server.md
- Developed by: NexGenData (community)
- Categories: AI, Developer tools, MCP servers
- Stats: 45 total users, 10 monthly users, 100.0% runs succeeded, 2 bookmarks
- User rating: No ratings yet
Pricing
from $20.00 / 1,000 tool calls
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event
What's an Apify Actor?
An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.
How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.
For examples already wired to this Actor's own input schema, see the API section below.
Each client library has reference documentation the quickstart does not restate: JavaScript/TypeScript (npm install apify-client) and Python (pip install apify-client).
README
Academic Research MCP
A Model Context Protocol server that lets AI agents search academic literature — arXiv preprints and OpenAlex/Crossref (an open alternative to Google Scholar — Google Scholar itself is not queried) — as callable tools. For research assistants and literature-review agents.
📊 Sample Output
status | message | how_to_connect | apify_url | transport | tools |
|---|---|---|---|---|---|
| MCP Server Running | This is an MCP server, not a scraper. It does not produce results whe… | Connect from an MCP-compatible AI client (Claude Desktop, Cursor, Win… | https://apify.com/nexgendata/academic-research-mcp-server | streamable-http | ['search_arxiv', 'search_google_scholar'] |
Real rows from run B0wMSmZ2C7Yb4HI3x on build 0.0.22 (2026-09-04), unedited apart from masked emails/phones and shortened long text; fields the source does not publish are empty.
🔧 Input reference
| Field | Type | Default | What it does |
|---|---|---|---|
enableServer (required) | boolean | true | Enable MCP server mode |
port | integer | 5000 | Port for MCP server |
🧾 JSON sample record
One real record from run B0wMSmZ2C7Yb4HI3x (emails/phones masked, long text shortened):
{
"status": "MCP Server Running",
"message": "This is an MCP server, not a scraper. It does not produce results when run directly.",
"how_to_connect": "Connect from an MCP-compatible AI client (Claude Desktop, Cursor, Windsurf, etc.)",
"server_url": "https://nexgendata--academic-research-mcp-server.apify.actor/mcp",
"apify_url": "https://apify.com/nexgendata/academic-research-mcp-server",
"documentation": "See the README for setup instructions and example configurations.",
"transport": "streamable-http",
"endpoint": "/mcp",
"tools": [
"search_arxiv",
"search_google_scholar"
]
}
💰 Pricing
| Event | Price (USD) | When it is charged |
|---|---|---|
Actor Start (apify-actor-start) | $5e-05 | Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event). |
Tool call (tool-call) | $0.02 | Per-tool invocation via MCP Standby |
Pay-per-event: you pay only for what the run delivers. A run that delivers nothing bills no result events (only the actor-start event, when the actor defines one). Example: a run that delivers 100 results costs 100 × $0.02 = $2.00 plus the start fee.
🔗 Related Actors
More from the NexGenData MCP servers for AI agents family:
- Automotive MCP — Car Search & Market Data for AI Agents
- Banking & Financial Stability MCP — for AI Agents
- China A-Share Market MCP — Screener & Filings for AI Agents
32 more in this family on the NexGenData Store page.
🛠 Tools (2)
search_arxiv— Search arXiv preprints by query.search_google_scholar— Search OpenAlex (~250M works, Crossref fallback) for papers, citation counts and DOIs. The tool keeps its name for client compatibility; it does not read Google Scholar.
🔌 Connect (Claude Desktop / Cursor / n8n / OpenAI Agents SDK)
Add this MCP server to your client config:
{
"mcpServers": {
"academic-research": {
"url": "https://nexgendata--academic-research-mcp-server.apify.actor/mcp"
}
}
}
Sample agent prompt:
Find the five most-cited 2024 arXiv papers on retrieval-augmented generation.
Pricing: $0.02 per tool call (Pay-Per-Event). Runs in Standby mode.
Related NexGenData Actors
| Use case | Actor |
|---|---|
| Underlying arXiv scraper | arxiv-scraper |
| Underlying paper search | OpenAlex API + Crossref API (keyless), same engine as Academic Paper Search |
| News MCP server | news-mcp-server |
| Web-scraping MCP server | web-scraping-mcp-server |
| 26-server gateway | enterprise-mcp-gateway |
FAQ
What tools does this server expose?
Two: search_arxiv and search_google_scholar. Nothing else.
Which sources does it cover? arXiv and OpenAlex/Crossref.
Is the schema stable for AI agents? Yes — each tool returns a stable JSON shape your prompt can rely on.
Auth? Apify token only.
Cost? Pay-per-event — you only pay for the tool calls your agent actually makes.
About NexGenData
NexGenData publishes a large catalog of data and buyer-intent actors, all pay-per-result. Browse the full catalog at https://apify.com/nexgendata
How NexGenData Pricing Works
Every NexGenData actor uses pay-per-event pricing — 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 / tool call: charged per MCP tool call
- No charge for retries, internal proxy rotation, or failed sub-requests — those are absorbed by the platform
Apify Platform Bonus
New to Apify? Sign up with the NexGenData referral link — you get free platform credits on signup and help fund the maintenance of this actor fleet.
Integration Surface
Every actor in the NexGenData catalog can be triggered from:
- Apify console — point-and-click run
- Apify API — REST + webhooks
- Apify Python / JS SDKs — programmatic batch
- Zapier, Make.com, n8n — official integrations
- MCP — exposed as MCP tools for Claude / ChatGPT / Cursor agents
- Schedules — built-in cron for daily / weekly / monthly runs
- Webhooks — POST results to any HTTPS endpoint on dataset write
Support
NexGenData maintains a large catalog of Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get a response within 24 hours.
Home: thenextgennexus.com Full catalog: apify.com/nexgendata
Actor input Schema
Actor input object example
{}
API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
JavaScript example
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
token: '<YOUR_API_TOKEN>',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("nexgendata/academic-research-mcp-server").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
Python example
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("nexgendata/academic-research-mcp-server").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
CLI example
echo '{}' |
apify call nexgendata/academic-research-mcp-server --silent --output-dataset
MCP server setup
{
"mcpServers": {
"apify": {
"type": "http",
"url": "https://mcp.apify.com/?tools=fetch-actor-details,nexgendata/academic-research-mcp-server"
}
}
}
The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an Authorization: Bearer <APIFY_API_TOKEN> header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).
OpenAPI specification
Download the OpenAPI definition: https://api.apify.com/v2/actors/MJi4dzjS5RnatpNHc/builds/4jCGQgMu4E8AKA3l5/openapi.json