Pricing

$9.95/month + usage

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Summary XL

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Developed by

AIRabbit

SummaryXL analyzes large amounts of data from any API, website, or files, creating concise summaries based on user input. Integrated with platforms like Apify, it processes datasets such as social media posts, focusing on specific topics.

0.0 (0)

Pricing

$9.95/month + usage

Total users

Monthly users

Runs succeeded

>99%

Last modified

4 months ago

Automation

Integrations

You can access the Summary XL 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

$# Start Server-Sent Events (SSE) session and keep it running
<curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=flamboyant_leaf/summary-xl"

# Session id example output:
# event: endpoint
# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1fa

Using Summary XL via Model Context Protocol (MCP) server

MCP server lets you use Summary XL 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 Summary XL Actor with the provided input.

$curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{
$  "jsonrpc": "2.0",
$  "id": 1,
$  "method": "tools/call",
$  "params": {
$    "arguments": {
$      "openaiApiKey": "xxx"
$},
$    "name": "flamboyant_leaf/summary-xl"
$  }
$}'

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

$event: message
$data: {
$  "result": {
$    "content": [
$      {
$        "type": "text",
$        "text": "ACTOR_RESPONSE"
$      }
$    ]
$  }
$}

Configure local MCP Server via standard input/output for Summary XL

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:

{
  "mcpServers": {
    "actors-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--actors",
        "flamboyant_leaf/summary-xl"
      ],
      "env": {
        "APIFY_TOKEN": "<YOUR_API_TOKEN>"
      }
    }
  }
}

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.

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