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ai-quizgenie

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ai-quizgenie

ai-quizgenie

bala-ceg/ai-quizgenie

Developed by

Balaji Seetharaman

Maintained by Community

ai-quizgenie is an Apify Actor that extracts content from webpages and PDFs to generate multiple-choice quiz questions (MCQs) using LLMs (GPT-3.5, GPT-4, etc.).

0.0 (0)

Pricing

Pay per event

0

Monthly users

4

Runs succeeded

81%

Last modified

18 days ago

You can access the ai-quizgenie programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

1# Start Server-Sent Events (SSE) session and keep it running
2curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=bala-ceg/ai-quizgenie"
3
4# Session id example output:
5# event: endpoint
6# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1fa

Using ai-quizgenie via Model Context Protocol (MCP) server

MCP server lets you use ai-quizgenie within your AI workflows. Send API requests to trigger actions and receive real-time results. Take the received sessionId and use it to communicate with the MCP server. The message starts the ai-quizgenie Actor with the provided input.

1curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{
2  "jsonrpc": "2.0",
3  "id": 1,
4  "method": "tools/call",
5  "params": {
6    "arguments": {
7      "url": "https://en.wikipedia.org/wiki/Artificial_intelligence",
8      "num_questions": 5,
9      "difficulty": "Medium"
10},
11    "name": "bala-ceg/ai-quizgenie"
12  }
13}'

The response should be: Accepted. You should received response via SSE (JSON) as:

1event: message
2data: {
3  "result": {
4    "content": [
5      {
6        "type": "text",
7        "text": "ACTOR_RESPONSE"
8      }
9    ]
10  }
11}

Configure local MCP Server via standard input/output for ai-quizgenie

You can connect to the MCP Server using clients like ClaudeDesktop and LibreChat or build your own. The server can run both locally and remotely, giving you full flexibility. Set up the server in the client configuration as follows:

1{
2  "mcpServers": {
3    "actors-mcp-server": {
4      "command": "npx",
5      "args": [
6        "-y",
7        "@apify/actors-mcp-server",
8        "--actors",
9        "bala-ceg/ai-quizgenie"
10      ],
11      "env": {
12        "APIFY_TOKEN": "<YOUR_API_TOKEN>"
13      }
14    }
15  }
16}

You can further access the MCP client through the Tester MCP Client, a chat user interface to interact with the server.

To get started, check out the documentation and example clients. If you are interested in learning more about our MCP server, check out our blog post.

Pricing

Pricing model

Pay per event 

This Actor is paid per result. You are not charged for the Apify platform usage, but only a fixed price for each dataset of 1,000 items in the Actor outputs.

actor-start-gb

$0.200

charge for actor usage