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LLM Dataset Processor

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LLM Dataset Processor

LLM Dataset Processor

Developed by

Dušan Vystrčil

Dušan Vystrčil

Maintained by Community

Allows you to process output of other actors or stored dataset with single LLM prompt. It's useful if you need to enrich data, summarize content, extract specific information, or manipulate data in a structured way using AI.

0.0 (0)

Pricing

Pay per usage

4

Total users

50

Monthly users

23

Runs succeeded

75%

Issues response

2.6 days

Last modified

a month ago

You can access the LLM Dataset Processor 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.

{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/sse?actors=dusan.vystrcil/llm-dataset-processor",
"--header",
"Authorization: Bearer <YOUR_API_TOKEN>"
]
}
}
}

Configure MCP server with LLM Dataset Processor

You have a few options for interacting with the MCP server:

  • Use mcp.apify.com via mcp-remote from your local machine to connect and authenticate using OAuth or an API token (as shown in the JSON configuration above).

  • Set up the connection directly in your MCP client UI by providing the URL https://mcp.apify.com/sse?actors=dusan.vystrcil/llm-dataset-processor along with an API token (or use OAuth).

  • Connect to mcp.apify.com via Server-Sent Events (SSE), as shown below:

{
"mcpServers": {
"apify": {
"type": "sse",
"url": "https://mcp.apify.com/sse?actors=dusan.vystrcil/llm-dataset-processor",
"headers": {
"Authorization": "Bearer <YOUR_API_TOKEN>"
}
}
}
}

You can connect to the Apify MCP Server using clients like Tester MCP Client, or any other MCP client of your choice.

If you want to learn more about our Apify MCP implementation, check out our MCP documentation. To learn more about the Model Context Protocol in general, refer to the official MCP documentation or read our blog post.