Academic Research MCP Server

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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. Actors are written with capital "A".

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. The best way to integrate Actors is as follows.

A Model Context Protocol server that lets AI agents search academic literature — arXiv preprints and Google Scholar — as callable tools. For research assistants and literature-review agents.

- search_arxiv— Search arXiv preprints by query.
- search_google_scholar— Search Google Scholar for papers and citations.

🔌 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" } } }

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.

What tools does this server expose?Two:search_arxivandsearch_google_scholar. Nothing else.

Which sources does it cover?arXiv and Google Scholar.

Is the schema stable for AI agents?Yes — each tool returns a stable JSON shape your prompt can rely on.

Cost?Pay-per-event — you only pay for the tool calls your agent actually makes.

Every NexGenData actor usespay-per-eventpricing — 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 chargefor retries, internal proxy rotation, or failed sub-requests — those are absorbed by the platform

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

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.

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.

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

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

echo '{}' | apify call nexgendata/academic-research-mcp-server --silent --output-dataset

{ "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,nexgendata/academic-research-mcp-server" } } }

Search global news using natural language. Webz.io News Search API returns the most relevant articles and content, with filters for source, country, language, date, sentiment, and category.

A flexible service for searching and analyzing academic papers on arXiv.

Source-first MCP knowledge control tower for watchlists, briefings, grounded search, and evidence-backed Ask.

A minimal MCP server for interacting with Wikipedia. It provides simple tools to search for articles and retrieve full content, making it easy for AI agents to access reliable, structured knowledge.

Fetch, convert, and search AWS documentation pages, with recommendations for related content.

Search campgrounds around the world on campertunity, check availability, and provide booking links.

Lightning-Fast, High-Accuracy Deep Research Agent 👉 8–10x faster 👉 Greater depth & accuracy 👉 Unlimited parallel runs

The Ferryhopper MCP Server exposes ferry routes, schedules and booking redirects so an AI assistant can discover connections across Europe and the Mediterranean and send users to Ferryhopper to complete bookings.

All-in-One SEO & Web Intelligence Toolkit API from FetchSERP.

MCP server that provides read-only access to HyperKitty, the web-based email archive component of Mailman 3.