You can access the Append to dataset 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.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/sse?actors=valek.josef/append-to-dataset",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

Configure MCP server with Append to dataset

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=valek.josef/append-to-dataset 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=valek.josef/append-to-dataset",
            "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.

XMLs To Dataset

mtrunkat/xmls-to-dataset

Go to actor anytime you need to download XML files and store them in the dataset.

Marek Trunkát

102

LLM Dataset Processor

dusan.vystrcil/llm-dataset-processor

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.

Dušan Vystrčil

Dataset Query Engine

jiri.spilka/dataset-query-engine

Use natural language queries to retrieve results from an Apify dataset. This Actor provides a query engine that loads a dataset, executes SQL queries, and synthesizes results.

Jiří Spilka

4.6

Forward Dataset to Actor or Task

valek.josef/forward-dataset-to-actor-or-task

Forwards contents of specified dataset to a specified field on the input of another Actor or task.

Josef Válek

Sort Dataset Items

lukaskrivka/sort-dataset-items

Add this actor as a webhook to your scraper to sort the dataset by index field

Lukáš Křivka

Python Example

apify/python-example

Example Actor written in Python, showing how to read the Actor input and push to the Actor's default dataset.

Apify

105

Dataset Processor in Python

drobnikj/dataset-processor-python

This actor utilizes Python to process the dataset.

Jakub Drobník

Validate Dataset(s) with JSON Schema

jaroslavhejlek/validate-dataset-with-json-schema

This Actor validates items in one or more datasets against a provided JSON Schema. Use it if you planning to add a dataset validation schema to your actor and you want test it.

Jaroslav Hejlek

Failed Runs Monitor

jannovotny/failed-runs-monitor

This actor will let you know about failed or time outed runs of your actors and tasks via Slack or email. It can also notice you about successful runs with empty dataset, check JSON schema of dataset items, or about runs that are running for too long.