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RabbitMQ Integration

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Developed by

Brasil Scrapers

This integration processes data from an Apify dataset and delivers it to a RabbitMQ queue. It is configured via a schema that includes details about the dataset and RabbitMQ connection settings.

0.0 (0)

Pricing

$10.00/month + usage

Total users

Monthly users

Last modified

6 months ago

Integrations

Automation

You can access the RabbitMQ Integration 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": {
        "local-actors-mcp-server": {
            "command": "npx",
            "args": [
                "-y",
                "@apify/actors-mcp-server",
                "--actors",
                "brasil-scrapers/rabbitmq-integration"
            ],
            "env": {
                "APIFY_TOKEN": "<YOUR_API_TOKEN>"
            }
        }
    }
}

Configure MCP server with RabbitMQ Integration

You can interact with the MCP server via standard input/output - stdio (as shown above), which is ideal for local integrations and command-line tools such as the Claude desktop client, or you can interact with the server through Server-Sent Events (SSE) to send messages and receive responses, which looks as follows:

{
    "mcpServers": {
        "remote-actors-mcp-server": {
            "type": "sse",
            "url": "https://mcp.apify.com/sse?actors=brasil-scrapers/rabbitmq-integration",
            "headers": {
                "Authorization": "Bearer <YOUR_API_TOKEN>"
            }
        }
    }
}

You can connect to the Apify MCP Server using clients like Tester MCP Client, or any other supported 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.

Kafka Integration

brasil-scrapers/kafka-integration

This integration facilitates the processing of data from an Apify dataset and its delivery to a Kafka topic. It is configured via a schema that includes details about the dataset, Kafka configuration, and batch processing size.

Brasil Scrapers

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

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

CSV File to Dataset

lukaskrivka/csv-file-to-dataset

Upload a local or remote CSV/text file and convert it to Apify Dataset for further use.

Lukáš Křivka

Forward dataset as POST data

anchor/forward-dataset-webhook

This actor forwards the results of an Actor to an endpoint, instead of having to fetch the results manually. It will download the dataset and attach it to the body of a POST request you will specify. It acts as a new webhook. Simplify your Actor process !!!

Anchor

Pinecone Integration

apify/pinecone-integration

This integration transfers data from Apify Actors to a Pinecone and is a good starting point for a question-answering, search, or RAG use case.

Apify

393

3.8

S3 Uploader

apify/s3-uploader

Upload data from an Apify dataset to an Amazon S3 bucket. Providing various filters and transformation options, this Actor allows precise control over data structure, formatting, and upload settings to ensure seamless integration into your data pipeline.

Apify

Weaviate Integration

apify/weaviate-integration

This integration transfers data from Apify Actors to a Weaviate and is a good starting point for a question-answering, search, or RAG use case.

Apify

4.7

Chroma Integration

apify/chroma-integration

This integration transfers data from Apify Actors to a Chroma and is a good starting point for a question-answering, search, or RAG use case.

Apify

4.7

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.