Deprecated

Pricing

$4.00 / 1,000 results

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LinkedIn Post Scraper - Extract Comments

Deprecated

See alternative Actors

Developed by

Ignacio O.

LinkedIn Post Scraper extracts comments and user profile data from LinkedIn posts. Ideal for engagement analysis, lead generation, and market research. Supports cookies for authentication, proxy integration, and customizable scraping settings to fit your needs. 🚀

0.0 (0)

Pricing

$4.00 / 1,000 results

Total users

Monthly users

Runs succeeded

>99%

Last modified

a month ago

Lead generation

Social media

Automation

You can access the LinkedIn Post Scraper - Extract Comments 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=ignacioruben7/apify-linkedin-post-scraper"

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

Using LinkedIn Post Scraper - Extract Comments via Model Context Protocol (MCP) server

MCP server lets you use LinkedIn Post Scraper - Extract Comments 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 LinkedIn Post Scraper - Extract Comments 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": {
$      "startUrls": [
$            {
$                  "url": "https://www.linkedin.com/posts/ignacioruben7_n8n-saas-ecommerce-activity-7303249729092505602-oPsB"
$            }
$      ]
$},
$    "name": "ignacioruben7/apify-linkedin-post-scraper"
$  }
$}'

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 LinkedIn Post Scraper - Extract Comments

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",
        "ignacioruben7/apify-linkedin-post-scraper"
      ],
      "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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