Academic Research MCP Server
Forschungspapiere von arXiv, Google Scholar und Wikipedia mit Zitationsmetriken
Dokumentation
Academic Research MCP Server — arXiv & Google Scholar (nexgendata/academic-research-mcp-server) Actor
MCP server exposing two tools: search_arxiv and search_google_scholar. Find scholarly papers from arXiv and Google Scholar from Claude, Cursor and any MCP agent. Returns clean JSON for LLM tool-use. Pay-per-event, no monthly minimum.
- URL: https://apify.com/nexgendata/academic-research-mcp-server.md
- Developed by: NexGenData (community)
- Categories: AI, Developer tools, MCP servers
- Stats: 28 total users, 12 monthly users, 100.0% runs succeeded, 1 bookmarks
- User rating: No ratings yet
Pricing
from $10.00 / 1,000 results
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/platform/actors/running/actors-in-store#pay-per-event
What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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".
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. The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official JavaScript/TypeScript client:
npm install apify-client
In Python projects, use official Python client library:
pip install apify-client
In shell scripts, use Apify CLI:
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## 🎓 Academic Research MCP Server — arXiv & Google Scholar for AI Agents
Connect AI agents to academic-paper search through the Model Context Protocol. Exposes two tools — `search_arxiv` and `search_google_scholar` — so Claude, ChatGPT, Cursor, or any MCP-compatible agent can find scholarly papers from arXiv and Google Scholar.
### Why This MCP Server
- **MCP-native** — connect from Claude Desktop / Cursor / any MCP client, no REST plumbing
- **Two sources** — arXiv preprints and Google Scholar results
- **Structured JSON** — clean output tuned for LLM tool-use, no PDF parsing or feed pagination
- **Pay-per-event** — no subscription, no seat fees, no monthly minimum
### What You Get
Tools exposed to your AI agent:
- **`search_arxiv`** — keyword search over arXiv preprints (runs on the arXiv scraper upstream)
- **`search_google_scholar`** — keyword search over Google Scholar results (runs on the Google Scholar scraper upstream)
Each response is clean JSON tuned for LLM tool-use.
### Use Cases
- **AI research assistants** — pull recent papers on a topic and summarize
- **Lit-review copilots** — search arXiv and Google Scholar to gather sources for a review
- **Topic monitoring agents** — periodically search a field for new papers
### Quick Start (MCP)
Configure Claude Desktop / Cursor / any MCP-compatible agent to use this Actor as an MCP server:
```json
{
"mcpServers": {
"academic-research-nexgendata": {
"url": "https://nexgendata--academic-research-mcp-server.apify.actor/mcp",
"headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
}
}
}
Pricing
Pay-per-event. Small Actor Start fee plus a per-tool-call fee. No monthly minimum, no seat fee.
Related NexGenData Actors
| Use case | Actor |
|---|---|
| Underlying arXiv scraper | arxiv-scraper |
| Underlying Google Scholar scraper | google-scholar-scraper |
| 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 Google Scholar.
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?fpr=2ayu9b
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("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=nexgendata/academic-research-mcp-server",
"--header",
"Authorization: Bearer <YOUR_API_TOKEN>"
]
}
}
}
OpenAPI specification
{
"openapi": "3.0.1",
"info": {
"title": "Academic Research MCP Server — arXiv & Google Scholar",
"description": "MCP server exposing two tools: search_arxiv and search_google_scholar. Find scholarly papers from arXiv and Google Scholar from Claude, Cursor and any MCP agent. Returns clean JSON for LLM tool-use. Pay-per-event, no monthly minimum.",
"version": "0.0",
"x-build-id": "TEGt6qaf4TluQ3n48"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/nexgendata~academic-research-mcp-server/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-nexgendata-academic-research-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/nexgendata~academic-research-mcp-server/runs": {
"post": {
"operationId": "runs-sync-nexgendata-academic-research-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/nexgendata~academic-research-mcp-server/run-sync": {
"post": {
"operationId": "run-sync-nexgendata-academic-research-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}